Knowledge Democracy and Epistemic In/Justice: Reflections on a Conversation
Bibliographic record
Abstract
The focus of the article is on how knowledge is created, who creates knowledge, how knowledge is co-constructed, whose knowledge is excluded and how knowledge is being used to challenge inequalities and strengthen social movement capacity. This article grew from a fascinating conversation that the three of us had in Montreal in September of 2019. We decided to share our stories about knowledge and justice with a wider audience in part as a way for us to reflect further on the meaning of our initial conversation, but also to invite others into the discussion. Baptiste Godrie works in a research centre (CREMIS) affiliated with Quebec’s health care and social services system, Isabel Heck works with the anti-poverty organization Parole d’excluEs, both affiliated to universities, and Budd Hall works at the University of Victoria and is the Co-Chair of the UNESCO Chair in Community-Based research and social responsibility in higher education.
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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 | 0.051 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.083 | 0.133 |
| Scholarly communication | 0.036 | 0.035 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.025 | 0.049 |
| Insufficient payload (model declined to judge) | 0.005 | 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".