Bibliographic record
Abstract
In this paper I've tried to spell out what I think we're confronting in attempting to make change in the context of global capitalism. I've used the notion of making change from below because the kind of government organization that made taking it over appear practicable has largely disappeared in a fragmentation of the state at many levels both within Canada and at international levels. Moreover the terrain of struggle has shifted from the directly physical to the contemporary text-mediated relations that pervade our societies. Right now we are also going through a rapid reorganization of governance replacing bureaucratic and professional organization with new managerial forms that subordinate both government and public institutions to the service of global capital. We can, however, find models of making change from below that have been effective. I look first to the Women's Movement, proposing that in addition to the specifics of its achievements, women in Canada are recognized and recognize ourselves as political subjects and agents. I then introduce more current examples of change initiated by non-governmental organizations, including unions. While specific objectives may be achieved, in the longer run these forms of organizing to make change are also important in building people's experience of acting and organizing, in extending connections among activists, and in grounding people's capacities to experience themselves as political subjects.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".