CPanel

cPanel is a web hosting control panel software developed by cPanel, L.L.C. It provides a graphical interface (GUI) and automation tools designed to simplify the process of hosting a web site for the website owner or "end user". It enables administration through a standard web browser using a three-tier structure. While cPanel is limited to managing a single hosting account, cPanel & WHM allow the administration of the entire server. In addition to the GUI, cPanel also has command line and API-based access that allows third-party software vendors, web hosting organizations, and developers to automate standard system administration processes. cPanel & WHM is designed to function either as a dedicated server or virtual private server. The latest cPanel & WHM version supports installation on AlmaLinux, Rocky Linux, CloudLinux OS, and Ubuntu. == History == cPanel is currently developed by cPanel, L.L.C., a privately owned company headquartered in Houston, Texas, United States. WebPros is the parent company of cPanel, L.L.C. It was originally designed in 1996 as the control panel for Speed Hosting, a now-defunct web hosting company. The original author of cPanel, J. Nick Koston, had a stake in Speed Hosting. Webking quickly began using cPanel after its merger with Speed Hosting. The new company moved its servers to Virtual Development Inc. (VDI), a now-defunct hosting facility. Following an agreement between Koston and VDI, cPanel was only available to customers hosted directly at VDI. At the time, there was little competition in the control panel market, with the main choices being VDI and Alabanza. Eventually, due to Koston leaving for college, he and William Jensen signed an agreement in which cPanel was split into a separate program called WebPanel; this version was run by VDI. Without the lead programmer, VDI was not able to continue any work on cPanel and eventually stopped supporting it completely. Koston kept working on cPanel while also working at BurstNET. Eventually, he left BurstNET to focus fully on cPanel. cPanel 3 was released in 1999: main additions over cPanel 2 were an automatic upgrade and the Web Host Manager (WHM). The interface was also improved when Carlos Rego of WizardsHosting made what became the default theme of cPanel. With the release of cPanel 11, cPanel adopted a four-tier versioning system, "Parent.Major.Minor.Patch" (e.g., 11.32.0.3). As of version 11.52, the "Parent" representation is deprecated, with 11.54 stylized as "Version 54." cPanel 11.30 is the last major version to support FreeBSD. On August 20, 2018 cPanel L.L.C. announced that it had signed an agreement to be acquired by a group led by Oakley Capital (who also own Plesk and SolusVM). While Koston sold his interest in cPanel, he will continue to be an owner of the company that owns cPanel. In April 2026, a severe vulnerability was discovered that affected all cPanel and WHM versions after 11.40, affectively allowing unauthenticated remote attackers to access the control panel. According to some web hosters the vulnerability was already being actively exploited, with some attempts even dating back to late February 2026. == Add-ons == cPanel provides front-ends for a number of common operations, including the management of PGP keys, crontab tasks, mail and FTP accounts, and mailing lists. Several add-ons exist, some for an additional fee, including auto installers such as Installatron, Fantastico, Softaculous, and WHMSonic (SHOUTcast/radio Control Panel Add-on). The add-ons need to be enabled by the server administrator in WHM to be accessible to the cPanel user. WHM manages some software packages separately from the underlying operating system, applying upgrades to Apache, PHP, MySQL and MariaDB, Exim, FTP, and related software packages automatically. This ensures that these packages are kept up-to-date and compatible with WHM, but makes it more difficult to install newer versions of these packages. It also makes it difficult to verify that the packages have not been tampered with, since the operating system's package management verification system cannot be used to do so. == WHM == WHM, short for WebHost Manager, is a web-based tool which is used for server administration. There are at least two tiers of WHM, often referred to as "root WHM", and non-root WHM (or Reseller WHM). Root WHM is used by server administrators and non-root WHM (with fewer privileges) is used by others, like entity departments, and resellers to manage hosting accounts often referred to as cPanel accounts on a web server. WHM is also used to manage SSL certificates (both server self generated and CA provided SSL certificates), cPanel users, hosting packages, DNS zones, themes, and authentication methods. The default automatic SSL (AutoSSL) provided by cPanel is powered by Let's Encrypt. Additionally, WHM can also be used to manage FTP, Mail (POP, IMAP, and SMTP) and SSH services on the server. As well as being accessible by the root administrator, WHM is also accessible to users with reseller privileges. Reseller users of cPanel have a smaller set of features than the root user, generally limited by the server administrator, to features which they determine will affect their customers' accounts rather than the server as a whole. From root WHM, the server administrator can perform maintenance operations such as upgrading and recompiling Apache and PHP, installing Perl modules, and upgrading RPMs installed on the system. == Enkompass == A version of cPanel & WHM for Microsoft Windows, called Enkompass, was declared end-of-life as of February 2014. Version 3 remained available for download, but without further development or support. In the preceding years, Enkompass had been available for free as product development slowed. == Pricing == On June 27, 2019 cPanel announced a new account-based pricing structure. After backlash from their customers, cPanel issued a second announcement but did not change the new structure.

Token maxxing

Token Maxxing or Token Maxing is a metric used in an attempt to track productivity in the workplace especially for those using Artificial Intelligence (AI) based services. AI services charge for each token which represent units of effort expended by an AI service to solve a problem. Some believe that token consumption equates to productivity and thus can be used as a metric to monitor an employee's work. Supporters believe that higher token usage indicates higher productivity and higher utilization of powerful AI services. This also suggests that those not consuming enough tokens may be less productive and underutilizing powerful AI services. This belief might lead to an environment that incentivizes higher token usage to predict increased productivity. Critics of token maxxing as a metric claim that prudent workers will maximize any metric that management wants increased to gain a workplace advantage. For example: Engineers in the tech industries pressed to consume as many tokens as possible might run several AI agents in tandem, enter longer input prompts, or automate their tasks to maximize their token consumption. To management, this higher token usage may indicate potential productivity, but in reality may cause additional token costs, worker burnout, or actually create more bloated code of lower quality. Another claim is AI service companies potentially benefit from such an emphasis on token consumption and actively encourage the trend. Some developers have publicly advocated the practice. Developer Sigrid Jin, who said he used 50 billion tokens in a single year, has argued that maximizing token consumption is the best way to understand the value of AI, advising others to spend as much on AI usage as they pay in rent to obtain a return on investment. == See Also == Goodhart's law Perverse incentive Jevons Paradox

Richard Zemel

Richard Stanley Zemel (born 1963) is a Canadian-American computer scientist and professor at Columbia University, Department of Computer Science, and a leading figure in the field of machine learning and computer vision. Zemel studied the history of science at Harvard University and obtained his B.A. in 1984. He continued his study at the Department of Computer Science of the University of Toronto under the supervision of Geoffrey Hinton. He obtained his M.Sc. and Ph.D. both in computer science in 1989 and 1994, respectively.

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Samer Hassan

Samer Hassan is a computer scientist, social scientist, activist and researcher, focused on the study of the collaborative economy, online communities and decentralized technologies. He is an associate professor at Universidad Complutense de Madrid (Spain) and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. He is the recipient of an ERC Grant of 1.5M€ with the P2P Models project, to research blockchain-based decentralized autonomous organizations for the collaborative economy. == Education and career == Hassan is a Spanish/Lebanese scholar with an interdisciplinary background, which combines computer sciences with social sciences and activism. He received a degree in Computer Science and MSc in Artificial Intelligence from the Universidad Complutense de Madrid (UCM) in Spain. He also studied three years of Political Science at the distance learning university UNED. He then pursued a PhD in Social Simulation at the department of Software Engineering and Artificial Intelligence of UCM, supervised by the computer scientist Juan Pavón and the sociologist Millán Arroyo-Menéndez. He has been researching in several institutions, funded by several scholarships and awards, most notably Harvard's Real Colegio Complutense, and the Spanish postdoctoral grants Juan de la Cierva and José Castillejo. Thus, he was a visiting researcher at the Centre for Research in Social Simulation, in the Department of Sociology at the University of Surrey in the UK, working under the supervision of Nigel Gilbert (2007-2008), and a lecturer at the American University of Science and Technology in Lebanon (2010–11). He was selected as Fellow at the Berkman Klein Center for Internet & Society at Harvard University (2015-2017) and is presently a Faculty Associate at the same structure. Starting in 2024, he joined, as affiliate faculty, the Institute for Digital Cooperative Economy (The New School), part of the Platform Cooperativism Consortium. == Activism and social engagement == As an activist, Hassan has been engaged in both offline (La Tabacalera de Lavapiés, Medialab-Prado) and online (Ourproject.org, Barrapunto, Wikipedia) initiatives. He was accredited as a grassroots facilitator by the Altekio Cooperative. He co-founded the Comunes Nonprofit in 2009 and the Move Commons webtool project in 2010. He has co-organized practitioner-oriented workshops on platform co-ops and free/open source decentralized tools for communities, and has presented his work in non-academic conferences of Mozilla, the Internet Archive, and others. As a privacy advocate, he co-created a course on cyber-ethics which has been teaching since 2013 (as of 2021). He was co-founder of the Sci-Fdi Spanish science-fiction magazine. His gender is non-binary and uses he/they pronouns. == Work == Hassan's interdisciplinary research spans multiple fields, including online communities, online governance, online collaboration, decentralized technologies, blockchain-based decentralized autonomous organizations, free/libre/open source software, Commons-based peer production, agent-based social simulation, social movements and cyberethics. He has published more than 60 works in these fields. Hassan's PhD thesis focused on the methodological challenges for building data-driven social simulation models. The main model built simulated the transition from modern values to postmodern values in Spain. His methodological work also explored the combination of different artificial intelligence technologies, i.e. software agents with fuzzy logic, data mining, natural language processing, and microsimulation. In his postdoctoral period, he focused on experimenting with multiple software systems to facilitate the collaborative economy, e.g. semantic-web labelling for commons-based initiatives, distribution of value in peer production communities, agent-supported online assemblies, decentralized real-time collaborative software, decentralized blockchain based reputation, or blockchain-enabled commons governance. Hassan was Principal Investigator of the UCM partner in the EU-funded P2Pvalue project on building decentralized web-tools for collaborative communities. As such, he led the team that created SwellRT, a federated backend-as-a-service focused to ease development of apps featuring real-time collaboration. Intellectual Property of this project was transferred to the Apache Software Foundation in 2017. As part of this research line, Hassan's team also develop two SwellRT-based apps, "Teem" for management of social collectives and Jetpad, a federated real time editor. He presented the innovations concerning these software at Harvard's Berkman Klein Center and Harvard's Center for Research on Computation and Society. Other research lines offered outcomes beyond publications. "Wikichron", coled by Javier Arroyo, is a web tool to visualize MediaWiki community metrics, currently in production and available for third-parties. "Decentralized Science", led by Hassan's PhD student Ámbar Tenorio-Fornés, is a framework to facilitate decentralized infrastructure and open peer review in the scientific publication process, which has been selected by the European Commission to receive funding as a spin-off social enterprise. His research on blockchain and crowdfunding models awarded him with a commission from Triple Canopy. His team pushed forward a mapping of the ecosystem of blockchain for social good, led by the Joint Research Centre and published by the European Commission. As part of his ERC project P2P Models, Hassan and his team –including Silvia Semenzin– are investigating whether blockchain technology and Decentralized Autonomous Organizations could contribute to improving the governance of commons-oriented communities, both online and offline. Their work has been showcased for tackling the impact of blockchain on governance, proposing alternatives to the current sharing economy, emerging forms of techno-social systems like NFTs or prediction markets, or giving relevance to gender issues in the field. Hassan was invited to present the project achievements in Harvard Kennedy School, MIT Media Lab, Harvard's Data Privacy Lab, Harvard's Center for Research on Computation and Society, and Harvard's SEAS EconCS. British MP and Opposition Leader Ed Miliband showcased his research and its potential impact on policy. The project made public its way of organizing and its core values. In particular, it has shown a commitment to diversity as a core value in hiring, or choosing case studies. == Selected works == Arroyo, Javier; Davó, David; Martínez-Vicente, Elena; Faqir-Rhazoui, Youssef; Hassan, Samer (8 November 2022). "DAO-Analyzer: Exploring Activity and Participation in Blockchain Organizations" (PDF). Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing. CSCW'22 Companion. New York, NY, USA: Association for Computing Machinery. pp. 193–196. doi:10.1145/3500868.3559707. ISBN 978-1-4503-9190-0. Rozas, David; Tenorio-Fornés, Antonio; Díaz-Molina, Silvia; Hassan, Samer (2021). "When Ostrom Meets Blockchain: Exploring the Potentials of Blockchain for Commons Governance". SAGE Open. 11 (1): 215824402110025. doi:10.1177/21582440211002526. ISSN 2158-2440. Faqir-Rhazoui, Youssef; Ariza-Garzón, Miller-Janny; Arroyo, Javier; Hassan, Samer (8 May 2021). "Effect of the Gas Price Surges on User Activity in the DAOs of the Ethereum Blockchain" (PDF). Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. CHI EA '21. New York, NY, USA: Association for Computing Machinery. pp. 1–7. doi:10.1145/3411763.3451755. ISBN 978-1-4503-8095-9. Hassan, Samer; Filippi, Primavera De (20 April 2021). "Decentralized Autonomous Organization". Internet Policy Review. 10 (2). doi:10.14763/2021.2.1556. hdl:10419/235960. ISSN 2197-6775. Joint Research Centre (European Commission); Hassan, Samer; Hakami, Anna; Brekke, Jaya Klara; De Filippi, Primavera; Lopéz Morales, Genoveva; Pólvora, Alexandre; Orgaz Alonso, Christian; Bodó, Balázs (2020). Scanning the European ecosystem of distributed ledger technologies for social and public good: what, why, where, how, and ways to move forward. LU: Publications Office of the European Union. doi:10.2760/300796. ISBN 978-92-76-21578-3. Filippi, Primavera De; Hassan, Samer (14 November 2016). "Blockchain technology as a regulatory technology: From code is law to law is code". First Monday. arXiv:1801.02507. doi:10.5210/fm.v21i12.7113. ISSN 1396-0466.

Feature hashing

In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values as indices directly (after a modulo operation), rather than looking the indices up in an associative array. In addition to its use for encoding non-numeric values, feature hashing can also be used for dimensionality reduction. This trick is often attributed to Weinberger et al. (2009), but there exists a much earlier description of this method published by John Moody in 1989. == Motivation == === Motivating example === In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets. Machine learning algorithms, however, are typically defined in terms of numerical vectors. Therefore, the bags of words for a set of documents is regarded as a term-document matrix where each row is a single document, and each column is a single feature/word; the entry i, j in such a matrix captures the frequency (or weight) of the j'th term of the vocabulary in document i. (An alternative convention swaps the rows and columns of the matrix, but this difference is immaterial.) Typically, these vectors are extremely sparse—according to Zipf's law. The common approach is to construct, at learning time or prior to that, a dictionary representation of the vocabulary of the training set, and use that to map words to indices. Hash tables and tries are common candidates for dictionary implementation. E.g., the three documents John likes to watch movies. Mary likes movies too. John also likes football. can be converted, using the dictionary to the term-document matrix ( John likes to watch movies Mary too also football 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 0 0 1 1 0 0 0 0 0 1 1 ) {\displaystyle {\begin{pmatrix}{\textrm {John}}&{\textrm {likes}}&{\textrm {to}}&{\textrm {watch}}&{\textrm {movies}}&{\textrm {Mary}}&{\textrm {too}}&{\textrm {also}}&{\textrm {football}}\\1&1&1&1&1&0&0&0&0\\0&1&0&0&1&1&1&0&0\\1&1&0&0&0&0&0&1&1\end{pmatrix}}} (Punctuation was removed, as is usual in document classification and clustering.) The problem with this process is that such dictionaries take up a large amount of storage space and grow in size as the training set grows. On the contrary, if the vocabulary is kept fixed and not increased with a growing training set, an adversary may try to invent new words or misspellings that are not in the stored vocabulary so as to circumvent a machine learned filter. To address this challenge, Yahoo! Research attempted to use feature hashing for their spam filters. Note that the hashing trick isn't limited to text classification and similar tasks at the document level, but can be applied to any problem that involves large (perhaps unbounded) numbers of features. === Mathematical motivation === Mathematically, a token is an element t {\displaystyle t} in a finite (or countably infinite) set T {\displaystyle T} . Suppose we only need to process a finite corpus, then we can put all tokens appearing in the corpus into T {\displaystyle T} , meaning that T {\displaystyle T} is finite. However, suppose we want to process all possible words made of the English letters, then T {\displaystyle T} is countably infinite. Most neural networks can only operate on real vector inputs, so we must construct a "dictionary" function ϕ : T → R n {\displaystyle \phi :T\to \mathbb {R} ^{n}} . When T {\displaystyle T} is finite, of size | T | = m ≤ n {\displaystyle |T|=m\leq n} , then we can use one-hot encoding to map it into R n {\displaystyle \mathbb {R} ^{n}} . First, arbitrarily enumerate T = { t 1 , t 2 , . . , t m } {\displaystyle T=\{t_{1},t_{2},..,t_{m}\}} , then define ϕ ( t i ) = e i {\displaystyle \phi (t_{i})=e_{i}} . In other words, we assign a unique index i {\displaystyle i} to each token, then map the token with index i {\displaystyle i} to the unit basis vector e i {\displaystyle e_{i}} . One-hot encoding is easy to interpret, but it requires one to maintain the arbitrary enumeration of T {\displaystyle T} . Given a token t ∈ T {\displaystyle t\in T} , to compute ϕ ( t ) {\displaystyle \phi (t)} , we must find out the index i {\displaystyle i} of the token t {\displaystyle t} . Thus, to implement ϕ {\displaystyle \phi } efficiently, we need a fast-to-compute bijection h : T → { 1 , . . . , m } {\displaystyle h:T\to \{1,...,m\}} , then we have ϕ ( t ) = e h ( t ) {\displaystyle \phi (t)=e_{h(t)}} . In fact, we can relax the requirement slightly: It suffices to have a fast-to-compute injection h : T → { 1 , . . . , n } {\displaystyle h:T\to \{1,...,n\}} , then use ϕ ( t ) = e h ( t ) {\displaystyle \phi (t)=e_{h(t)}} . In practice, there is no simple way to construct an efficient injection h : T → { 1 , . . . , n } {\displaystyle h:T\to \{1,...,n\}} . However, we do not need a strict injection, but only an approximate injection. That is, when t ≠ t ′ {\displaystyle t\neq t'} , we should probably have h ( t ) ≠ h ( t ′ ) {\displaystyle h(t)\neq h(t')} , so that probably ϕ ( t ) ≠ ϕ ( t ′ ) {\displaystyle \phi (t)\neq \phi (t')} . At this point, we have just specified that h {\displaystyle h} should be a hashing function. Thus we reach the idea of feature hashing. == Algorithms == === Feature hashing (Weinberger et al. 2009) === The basic feature hashing algorithm presented in (Weinberger et al. 2009) is defined as follows. First, one specifies two hash functions: the kernel hash h : T → { 1 , 2 , . . . , n } {\displaystyle h:T\to \{1,2,...,n\}} , and the sign hash ζ : T → { − 1 , + 1 } {\displaystyle \zeta :T\to \{-1,+1\}} . Next, one defines the feature hashing function: ϕ : T → R n , ϕ ( t ) = ζ ( t ) e h ( t ) {\displaystyle \phi :T\to \mathbb {R} ^{n},\quad \phi (t)=\zeta (t)e_{h(t)}} Finally, extend this feature hashing function to strings of tokens by ϕ : T ∗ → R n , ϕ ( t 1 , . . . , t k ) = ∑ j = 1 k ϕ ( t j ) {\displaystyle \phi :T^{}\to \mathbb {R} ^{n},\quad \phi (t_{1},...,t_{k})=\sum _{j=1}^{k}\phi (t_{j})} where T ∗ {\displaystyle T^{}} is the set of all finite strings consisting of tokens in T {\displaystyle T} . Equivalently, ϕ ( t 1 , . . . , t k ) = ∑ j = 1 k ζ ( t j ) e h ( t j ) = ∑ i = 1 n ( ∑ j : h ( t j ) = i ζ ( t j ) ) e i {\displaystyle \phi (t_{1},...,t_{k})=\sum _{j=1}^{k}\zeta (t_{j})e_{h(t_{j})}=\sum _{i=1}^{n}\left(\sum _{j:h(t_{j})=i}\zeta (t_{j})\right)e_{i}} ==== Geometric properties ==== We want to say something about the geometric property of ϕ {\displaystyle \phi } , but T {\displaystyle T} , by itself, is just a set of tokens, we cannot impose a geometric structure on it except the discrete topology, which is generated by the discrete metric. To make it nicer, we lift it to T → R T {\displaystyle T\to \mathbb {R} ^{T}} , and lift ϕ {\displaystyle \phi } from ϕ : T → R n {\displaystyle \phi :T\to \mathbb {R} ^{n}} to ϕ : R T → R n {\displaystyle \phi :\mathbb {R} ^{T}\to \mathbb {R} ^{n}} by linear extension: ϕ ( ( x t ) t ∈ T ) = ∑ t ∈ T x t ζ ( t ) e h ( t ) = ∑ i = 1 n ( ∑ t : h ( t ) = i x t ζ ( t ) ) e i {\displaystyle \phi ((x_{t})_{t\in T})=\sum _{t\in T}x_{t}\zeta (t)e_{h(t)}=\sum _{i=1}^{n}\left(\sum _{t:h(t)=i}x_{t}\zeta (t)\right)e_{i}} There is an infinite sum there, which must be handled at once. There are essentially only two ways to handle infinities. One may impose a metric, then take its completion, to allow well-behaved infinite sums, or one may demand that nothing is actually infinite, only potentially so. Here, we go for the potential-infinity way, by restricting R T {\displaystyle \mathbb {R} ^{T}} to contain only vectors with finite support: ∀ ( x t ) t ∈ T ∈ R T {\displaystyle \forall (x_{t})_{t\in T}\in \mathbb {R} ^{T}} , only finitely many entries of ( x t ) t ∈ T {\displaystyle (x_{t})_{t\in T}} are nonzero. Define an inner product on R T {\displaystyle \mathbb {R} ^{T}} in the obvious way: ⟨ e t , e t ′ ⟩ = { 1 , if t = t ′ , 0 , else. ⟨ x , x ′ ⟩ = ∑ t , t ′ ∈ T x t x t ′ ⟨ e t , e t ′ ⟩ {\displaystyle \langle e_{t},e_{t'}\rangle ={\begin{cases}1,{\text{ if }}t=t',\\0,{\text{ else.}}\end{cases}}\quad \langle x,x'\rangle =\sum _{t,t'\in T}x_{t}x_{t'}\langle e_{t},e_{t'}\rangle } As a side note, if T {\displaystyle T} is infinite, then the inner product space R T {\displaystyle \mathbb {R} ^{T}} is not complete. Taking its completion would get us to a Hilbert space, which allows well-behaved infinite sums. Now we have an inner product space, with enough structure to describe the geometry of the feature hashing function ϕ : R T → R n {\displaystyle \phi :\ma

Glottochronology

Glottochronology (from Attic Greek γλῶττα 'tongue, language' and χρόνος 'time') is the part of lexicostatistics which involves comparative linguistics and deals with the chronological relationship between languages. The idea was developed by Morris Swadesh in the 1950s in his article on Salish internal relationships. He developed the idea under two assumptions: there indeed exists a relatively stable basic vocabulary (referred to as Swadesh lists) in all languages of the world; and, any replacements happen in a way analogous to radioactive decay in a constant percentage per time elapsed. Using mathematics and statistics, Swadesh developed an equation to determine when languages separated and give an approximate time of when the separation occurred. His methods aimed to aid linguistic anthropologists by giving them a definitive way to determine a separation date between two languages. The formula provides an approximate number of centuries since two languages were supposed to have separated from a singular common ancestor. His methods also purported to provide information on when ancient languages may have existed. Despite multiple studies and literature containing the information of glottochronology, it is not widely used today and is surrounded with controversy. Glottochronology tracks language separation from thousands of years ago but many linguists are skeptical of the concept because it is more of a 'probability' rather than a 'certainty.' On the other hand, some linguists may say that glottochronology is gaining traction because of its relatedness to archaeological dates. Glottochronology is not as accurate as archaeological data, but some linguists still believe that it can provide a solid estimate. Over time many different extensions of the Swadesh method evolved; however, Swadesh's original method is so well known that 'glottochronology' is usually associated with him. == Methodology == The original method of glottochronology presumed that the core vocabulary of a language is replaced at a constant (or constant average) rate across all languages and cultures and so can be used to measure the passage of time. The process makes use of a list of lexical terms and morphemes which are similar to multiple languages. Lists were compiled by Morris Swadesh and assumed to be resistant against borrowing (originally designed in 1952 as a list of 200 items, but the refined 100-word list in Swadesh (1955) is much more common among modern day linguists). The core vocabulary was designed to encompass concepts common to every human language such as personal pronouns, body parts, heavenly bodies and living beings, verbs of basic actions, numerals, basic adjectives, kin terms, and natural occurrences and events. Through a basic word list, one eliminates concepts that are specific to a particular culture or time period. It has been found through differentiating word lists that the ideal is really impossible and that the meaning set may need to be tailored to the languages being compared. Word lists are not homogenous throughout studies and they are often changed and designed to suit both languages being studied. Linguists find that it is difficult to find a word list where all words used are culturally unbiased. Many alternative word lists have been compiled by other linguists and often use fewer meaning slots. The percentage of cognates (words with a common origin) in the word lists is then measured. The larger the percentage of cognates, the more recently the two languages being compared are presumed to have separated. === Glottochronologic constant === Determining word lists rely on morpheme decay or change in vocabulary. Morpheme decay must stay at a constant rate for glottochronology to be applied to a language. This leads to a critique of the glottochronologic formula because some linguists argue that the morpheme decay rate is not guaranteed to stay the same throughout history. American Linguist Robert Lees obtained a value for the "glottochronological constant" (r) of words by considering the known changes in 13 pairs of languages using the 200 word list. He obtained a value of 0.8048 ± 0.0176 with 90% confidence. For his 100-word list Swadesh obtained a value of 0.86, the higher value reflecting the elimination of semantically unstable words. === Divergence time === The basic formula of glottochronology proposed by Morris Swadesh is: t = − ln ⁡ ( c ) 2 ln ⁡ ( r ) {\displaystyle t=-{\frac {\ln(c)}{2\ln(r)}}} t = a given period of time from one stage of the language to another (measured in millennia), c = proportion of wordlist items retained at the end of that period and r = rate of replacement for that word list. By testing historically verifiable cases in which t is known by nonlinguistic data (such as the approximate distance from Classical Latin to modern Romance languages), Swadesh arrived at the empirical value of approximately 0.14 for L, (c?) which means that the rate of replacement constitutes around 14 words from the 100-wordlist per millennium. This is represented in the table below. === Results === Glottochronology was applied to a range of language families, including Salishan, Indo-European, Japonic, Afro-Asiatic, Chinese and Mayan and other American languages. For Amerind, correlations have been obtained with radiocarbon dating and blood groups as well as archaeology. === Example Wordlist === Below is an example of a basic word list composed of basic Turkish words and their English translations. == Discussion == The concept of language change is old, and its history is reviewed in Hymes (1973) and Wells (1973). In some sense, glottochronology is a reconstruction of history and can often be closely related to archaeology. Many linguistic studies find the success of glottochronology to be found alongside archaeological data. Glottochronology itself dates back to the mid-20th century. An introduction to the subject is given in Embleton (1986) and in McMahon and McMahon (2005). Glottochronology has been controversial ever since, partly because of issues of accuracy but also because of the question of whether its basis is sound (for example, Bergsland 1958; Bergsland and Vogt 1962; Fodor 1961; Chrétien 1962; Guy 1980). The concerns have been addressed by Dobson et al. (1972), Dyen (1973) and Kruskal, Dyen and Black (1973). The assumption of a single-word replacement rate can distort the divergence-time estimate when borrowed words are included (Thomason and Kaufman 1988). The presentations vary from "Why linguists don't do dates" to the one by Starostin discussed below. Since its original inception, glottochronology has been rejected by many linguists, mostly Indo-Europeanists of the school of the traditional comparative method. Criticisms have been answered in particular around three points of discussion: Criticism levelled against the higher stability of lexemes in Swadesh lists alone (Haarmann 1990) misses the point because a certain amount of losses only enables the computations (Sankoff 1970). The non-homogeneity of word lists often leads to lack of understanding between linguists. Linguists also have difficulties finding a completely unbiased list of basic cultural words. it can take a long time for linguists to find a viable word list which can take several test lists to find a usable list. Traditional glottochronology presumes that language changes at a stable rate. Thus, in Bergsland & Vogt (1962), the authors make an impressive demonstration, on the basis of actual language data verifiable by extralinguistic sources, that the "rate of change" for Icelandic constituted around 4% per millennium, but for closely connected Riksmal (Literary Norwegian), it would amount to as much as 20% (Swadesh's proposed "constant rate" was supposed to be around 14% per millennium). That and several other similar examples effectively proved that Swadesh's formula would not work on all available material, which is a serious accusation since evidence that can be used to "calibrate" the meaning of L (language history recorded during prolonged periods of time) is not overwhelmingly large in the first place. It is highly likely that the chance of replacement is different for every word or feature ("each word has its own history", among hundreds of other sources:). That global assumption has been modified and downgraded to single words, even in single languages, in many newer attempts (see below). There is a lack of understanding of Swadesh's mathematical/statistical methods. Some linguists reject the methods in full because the statistics lead to 'probabilities' when linguists trust 'certainties' more. A serious argument is that language change arises from socio-historical events that are, of course, unforeseeable and, therefore, uncomputable. == Modifications == Somewhere in between the original concept of Swadesh and the rejection of glottochronology in its entirety lies the idea that glottochronology as a formal method of linguistic