Bibliographic record
Abstract
AN INTRODUCTORY NOTE In an earlier book in this series, Spolsky (2004) began by observing that studies of the social life of language are often too ‘language-centred’. Any investigation of language that considers only language will be deficient, and inappropriate limitations and restrictions can cripple insights. This is not a problem for this area alone, of course, but it is especially significant in a context where the hope of application fuels much of the effort. Nonetheless, any cursory examination of, say, the language-planning literature or work in the social psychology of language will quickly reveal an undesirable narrowness of perspective. Studies of ‘endangered languages’ and ‘language revival’ seem particularly prone to tunnel vision, to the curious notion that these phenomena can be understood and then ameliorated in more or less isolated fashion. Except in the conceits of ‘pure’ linguistics, no analysis of language can rationally proceed from a ‘stand-alone’ perspective. Spolsky writes that while many scholars are now beginning to recognize the interaction of economic and political and other factors with language, it is easy and tempting to ignore them when we concentrate on language matters. (pp. ix–x) In fact, while one still reads too many disembodied, decontextualised and, therefore, essentially useless studies, the observation here is not quite accurate. For some writers – more nowadays than in the past, I would guess – ‘temptation’ is not an apt term at all, for the simple reason that a more extensive purview seems simply beyond them.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.456 | 0.281 |
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".