Bibliographic record
Abstract
At first glance, it may appear that the association of language with ethnic group affiliation is one of the more obvious relationships between language and culture. Practically all of the approximately 6,000 languages of the world, for example, are strongly associated with an ethnocultural group of some type. But this initial transparency is betrayed by the fact that language is neither a necessary nor a sufficient condition for ethnic group membership (Fishman 1999). Like sociocultural borders, linguistic boundaries are permeable, negotiated constructs typically defined more on the basis of sociopolitical and ideological considerations than on the basis of structural linguistic parameters. Even the dichotomy between ‘language’and ‘dialect’ turns out to be based more on cultural and political issues than on mutual intelligibility or structural linguistic properties. Thus, Sino-Tibetan language varieties such as Cantonese and Mandarin are commonly referred to as dialects of Chinese even though they may not be mutually intelligible, whereas Norwegian and Swedish are considered to be different languages although speakers usually understand each other. In the former case, there is an overarching cultural unity that transcends linguistic typology whereas, in the latter case, there is a national political border that reifies minimal structural diversity in linguistic varieties. By the same token, sociopolitical struggles about language – such as those over the status of Afrikaans in South Africa, the role of French and English in Canada, or the legitimacy of African American English (so-called ‘Ebonics’) in the United States – are ultimately not about language, but about ideology, identity, and sociopolitical power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".