Bibliographic record
Abstract
It is now more than 30 years since the launch of the bilateral:anada-U.S. Free Trade Agreement (CUFTA), predecessor to the multilateral North American Free Trade Agreement (NAFTA) and the (now abandoned) Free Trade Area of the Americas (FTAA). For a generation, these "free trade" initiatives provided an important part of the framework in which political movements developed in Canada, engendering debates and controversies which continue to this day. When a new moment of trade politics emerged with Donald Trump's challenge to NAFTA, some veterans from those earlier anti-free trade battles were unable to see the new, white nationalist terrain upon which Trump was operating. This article - organized principally around the author's own engagement with the anti-free trade movements of the 1980s - suggests that this inability to see clearly the new context of anti-free trade politics was rooted in the incomplete and contradictory left-nationalist theory which underpinned most anti-free trade politics of that earlier era. The article suggests that while there are national questions in Canada - in particular those associated with Indigenous peoples and with Quebec - the attempt to articulate a parallel "national question" in Canada as a whole has proven to be impossible.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.042 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".