MR. DAVIS GREETS A FRIEND IN HIS OFFICE M. Davis salue un ami à son bureau
Bibliographic record
Abstract
M. DAVIS SALUE UN AMI A SON BUREAU 1 M. Dupont, un ami de M. Davis, demeure k Londres. Cependanf II parle bien le fran^ais parce que ses parents sont canadiens. C’est un homme de trentecinq ans. 2 D sait que son ami M. Davis apprend le fran^ais. D desire voir si son ami fait des progr&s dans ses Etudes. Done, il entre un jour dans le bureau de M. Davis et le salue en frangais. Voici leur conversation: 3 — Comment 9a va? 4 — Trfes bien merci. Et vous? 5 — Comme ci, comme 9a. A propos, vous apprenez le fran9ais, n’est-ce pas? 6 — Bien stir. J ’apprends k 1 parler, k lire et k ta ire le fran9ais. 7 — Est-ce que le fran9ais est difficile k apprendre? 8 — Eh bien, non. Le fran9ais n’est pas trop difficile k apprendre. J ’aime beaucoup la langue fran9aise et je l’6tudie2 assidument. 9 — Qui est votre professeur de fran9ais?
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.168 | 0.045 |
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".