Bibliographic record
Abstract
t is impossible in a few pages to rebut the many mistaken extrapolations that Abu-Laban and Stasiulis make about my analyses of issues other than those addressed in my original article. Suffice it to say that my thesis is simple. It is that there is such a thing as an ethnic English-Canadian; that one can be an ethnic English-Canadian even if one does not have English or European or Christian background; and that one's ethnicity is rooted in social practice, not in biological ancestry. Ethnicity is a matter of private choice, yet Canada's current public multicultural policy a liberal policy of supporting individuals' private choices of ethnicity has the paradoxical effect of making people of non-European and non-Christian background feel more comfortable in Canada than they might otherwise be.
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 | 0.018 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.024 | 0.047 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".