Bibliographic record
Abstract
Ezio CappadociaIn the summer of 1959, for the first time in twenty-five years, I was back in Italy.The excitement of being in Florence was exhilarating.One day in June, I saw the tabloid international edition of the Globe and Mail.Its two-word headline "Duplessis Dead" was stunning.I, recently married, turned to my American wife and said: "I wish I were back home."That someone born in Italy and now finally discovering the country should wish to be back in Kingston, Ontario, rather than in Florence, because a Canadian politician had died, raises questions of identity.How and when an immigrant to Canada sees himself, and, even more, is seen by others to be "Canadian" remains a complex and not an easily-answered question.On May 12, 1991, Betty Disero, a Toronto politician who wants to be mayor, was described by a columnist in the Toronto Star as being "Italian."I inquired whether henceforth all politicians would be identified in that paper by the national origin of their parents.Would Stephen Lewis now be called "Polish"?A similar national identification had been made months before by Jeffrey Simpson in the Globe and Mail.To him being "Italian" was one of the many qualifications of Frank Iacobucci for a possible appointment to the Supreme Court.It was not a question of being a Canadian, or an Italo-Canadian, or of having Italian heritage, but of being "Ttalian."Annoyance at this sort of nonsense is tempered by the knowledge that politicians are ready to give themselves ethnic identification when it suits their convenience.On May
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".