References Bhabha, H. 1983: Difference, discrimination, and the discourse of colo-
Bibliographic record
Abstract
H.M. Hammerly (Simon Fraser University) has distributed a test intended for Anglophone students who have completed at least five years of French immersion programmes in Canada. His intention is to use the test to validate his claims that immersion education has failed to produce ade-quate language proficiency (see Hammerly, 1991). Since the potential impact of his findings could be great, it seems imperative to give a close look to the test on which claims will be based. I have reviewed Hammerly’s French immersion test, item by item, and offer this appraisal of it. The test has two parts to it. The first is called ’Noun gender’, and consists of a list of 20 French words with their English equivalent in parenthesis. The respondent is to supply the appropriate indefinite article. Each item is worth one point. The second part of the test is called ’Grammar and vocabulary’, and consists of 50 English sentences that the respondent is to translate into French. Each sentence is worth two points, and one point is deducted for each error in grammar or vocabulary.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".