Bibliographic record
Abstract
Symbolizing more than a singular date, 1968 was an international phenomenon. From Paris to New York, from Mexico City to Karachi, students, workers, and peasants mobilized and marched against war and imperialist aggression, for rights, entitlements, and participatory democracy, opposing bureaucratic officialdoms and effete elites. If ’68ers did not always articulate clearly what they were for, they certainly knew what they were against. “It was right to rebel” echoed across barricades and throughout sit-ins. Just how this dissonance sounded, however, always reverberated with the peculiarities of specific locales. Canada’s 1968 was no different. The Combined Universities Campaign for Nuclear Disarmament, founded in 1959, morphed into the “student syndicalism” of 1964’s Student Union for Peace Action (SUPA).1 Montreal, the centerpiece of Québécois radical nationalist grievance, fostered a C. L. R. James–influenced Caribbean struggle against racism. Fed up with discriminatory treatment, West Indian students occupied the Sir George Williams University computer complex in January–February 1969. Mayhem ensued. Campus property burned, millions of dollars going up in smoke. Almost 100 arrests followed, with roughly half of those charged being black.2
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".