Popular education and learning as the bridge between activism and knowledge production
Bibliographic record
Abstract
Academic research can make significant contributions to policymakers and other researchers interested in building evidence-based knowledge. However, it is difficult for students to imagine how their research can effectively contribute to social change while respecting curriculum requirements, especially with regard to maintaining methodological and scientific rigour and the validity demanded by academic standards. The rich work and experience of Aziz Choudry contribute directly to overcoming these obstacles and challenges, as he conceptualised research and knowledge production as an activity not exclusive to academia or research institutes, but widely present within social organisations and social movements.In this paper, we reflect on our experiences of conducting research within community organisations as graduate scholars. Building on our respective research experience, including popular education in our research practice, we highlight how popular education spaces offer opportunities for scholars to disseminate their research results and contribute to raising awareness, but also to achieve the standards of intellectual rigour expected by academia. The main goal of this paper is to position the field of social movement learning and knowledge production as key for students who want to develop engaged and relevant research.
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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".