Bibliographic record
Abstract
T]he most powerful, visionary dreams of a new society don't come from little think tanks of smart people or out of the atomized, individualistic world of consumer capitalism, where raging against the status quo is simply the hip thing to do.Revolutionary dreams erupt out of political engagement; collective social movements are incubators of new knowledge (Robin Kelley, 2002, p. 8).In his book, Freedom dreams: The black radical imagination, historian Robin Kelley (2002) points out that social movements generate new knowledge, questions, and theory. 2 He also emphasizes the need for concrete and critical engagement with the movements confronting the problems of oppressed peoples.In this article I discuss the politics of learning, knowledge production and research "from the ground up"that which takes place in social action/social movement contexts.While on the one hand, scholarship on social movements owes significant intellectual debts to the learning, knowledge production and theorizing which occur in the course of social struggles, on the other, the intellectual work, ideas and analysis which emerge from concrete engagement in these struggles are often overlooked and undervalued by the dominant theorizing practices of the academy.Further, drawing firstly from the author's involvement with research and activism around Montreal's 1
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".