Bibliographic record
Abstract
It is both a fact and a frequent lament that English speakers lag far behind others in foreign-language competence. The teaching and learning of foreign languages in North America and Britain have seemed more difficult and less attractive undertakings than they are in Europe. Do we observe here some genetic anglophone linguistic deficiency? Are the British and the Americans right when they say ‘I’m just no good at foreign languages’? Are they right to envy those clever Europeans, Africans and Asians who slide effortlessly from one mode to another? The answers here obviously involve environmental conditions, not genetic ones, but I present these rather silly notions because — to the extent to which they are believed, or half-believed, or inarticulately felt — they constitute a type of self-fulfilling prophecy which adds to the difficulty of language learning. I use the word ‘adds’ here because the real difficulties, the important contextual conditions, the soil in which such prophecies flourish, have to do with power and dominance. Anglophone linguistic laments perhaps involve some crocodile tears or, at least, can seem rather hollow: the regrets of those who lack competence, but who need not, after all, really bother to acquire it.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".