Democratizing knowledge: the experience of university-community research partnerships
Bibliographic record
Abstract
Globalization has been characterized by the development and rapid circulation of knowledge, emphasized with the use of new technologies. Knowledge is gaining an increasing space at the core of societies and the knowledge economy. This current reality encourages us to consider development practices and knowledge circulation under the perspective of democratization. The author of this essay discusses the several partnerships that have been established among UQAM and communities: these innovations are inspired by a vision of the democratization of education focusing on access to, research and circulation of knowledge. The case study focuses on the research process that is developed together with the communities involved, the challenges linked to the collaboration among two different organizations as well as the obstacles, the opportunities and the conditions that have contributed to the success of these initiatives. While developing this approach requires the availability of several partners, it has the advantage of broadening and democratizing the group of those producing and circulating academic knowledge. This institutional research model that has been developed in Quebec is arousing a lot of interest in the North and in the South as an alternative approach to promote the democratization of knowledge.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.026 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".