<scp>Janet L. Nicol</scp> (ed.), <i>One mind, two languages: Bilingual language processing</i>. Oxford: Blackwell, 2000. Pp. 264. Hb $77.95, Pb $35.95.
Bibliographic record
Abstract
When I have a dinner party, it's a matter of great importance to me that all the places are set with the same tableware. It's the same at a restaurant – a group of people sitting down for dinner should be served on the same dishes. It's neurotic, I know. But aside from some primitive sense of symmetrical comfort, the plates tell you much about the meal that is to follow: Fine porcelain sets expectations of grace and elegance; brightly colored stoneware establishes a casual ambience; and simple plates communicate a utilitarian attitude to the ensuing meal. The form serves as a gatekeeper, announcing at the outset the intended clientele. If a family with small children in search of cheeseburgers and fries wanders by chance into an establishment that places Royal Doulton in front them, they will know instantly that the content will not meet their expectations. Form matters, and content is partly conveyed by the form.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.145 | 0.126 |
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".