Bibliographic record
Abstract
Mixed MarriagesAs a Montreal anglophone, I went through Quebec's (and Canada's) FLQ crisis being pulled out of my elementary school several times due to bomb threats. 1 Driving in Montreal with my family as part of our regular routine in that period, I stared out the car window as we passed the army personnel carriers and Canadian soldiers in battle gear who were sent to the city as part of the War Measures Act.We drove stoically by, glancing at each other nervously but not speaking about what was going on all around us.I picked up on the hushed but omnipresent anxiety and tension of the adult world that surrounded me.We children were being kept safe in a watchful way that made my parents seem vulnerable.For a brief period of my childhood, at the intuitive and inchoate level at which children pick up on the world around them, I was aware that the safe enclave of my life was not secure.Such episodes provide a foundation for the oft-repeated claims of critics of the one-state model for Israel/Palestine that there is no example of a successful binational state in the world, Canada being forever, in their estimation, on the verge of a return to this snapshot of my childhood (and my parents') experience.Many Montreal anglophones -close to 400,000 -felt sufficiently unnerved by the crisis (and by the threatened loss of their place in the changing world of Quebec when the separatist Parti Québécois came to power in 1976) that they fled, creating a massive diaspora across the country and
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.556 | 0.333 |
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".