Bibliographic record
Abstract
Abstract The findings of this study add to the growing number of reports in which investigators claim to have located adult second language learners who, under rigorous test conditions, manage to pass as native speakers in L2. The aims of this paper were two, first, to provide a detailed account of how we tested and qualified our Anglophones as native-like speakers of French and, second, to suggest that, interesting as our data were, more questions emerge than do answers. Seven of 18 English/French bilinguals, having acquired L2 after the age of 16, were selected by means of a pre-test interview with three Francophones as “potentially of French-speaking background.” These seven, along with three Francophone controls, recited an 81-word passage in French onto a tape-recorder. Sixty-eight native-speaking French raters, of similar dialectal background and weak in English, each heard one of four tapes with differing random roders of the 10 passages, their task being to designate each voice as “Franco-phone” or “non-Francophone.” Four of our seven English-Franch bilinguals obtained ratings statistically comparable to those of our three Francophone controls.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".