Bibliographic record
Abstract
Coleman (2006: 11) has suggested that ‘ultimately, the world will become diglossic, with one language for local communication, culture and expression of identity, and another — English — for wider and more formal communication, especially in writing’. It is clear that many feel this way, although not all of them are pleased by the prospect, of course, and some feel that it is an outcome to be resisted. Relatedly, Nettle and Romaine (2002: 191) have argued that such a bilingual or diglossic situation essentially transforms ‘the majority of the world’s languages [into] minority languages’. I don’t think this is quite accurate or, if it is, it achieves accuracy only by sliding back and forth between different linguistic regions. Nonetheless, the increasing power and attraction of English certainly alters patterns of communication in important ways. It is undoubtedly accurate to add, as Nettle and Romaine do, that even those languages ‘protected by national boundaries and institutions exist in a diglossic relationship with English’. It is also correct for the authors to write that ‘this in itself is no cause for alarm’ (p. 191). If the balance between a ‘big’ language like English and ‘smaller’ or more localised ones could remain more or less stable, more or less diglossic, then there would indeed be little cause for alarm.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.098 | 0.018 |
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".