Bibliographic record
Abstract
Informed by my experiences in prison/university co-learning projects, this essay centres two community-based learning practices worth cultivating. First, what can happen when all participants truly prioritize what it means to build community as they address their shared project, co-discovering new ways of being and doing together, listening receptively and speaking authentically? How can project facilitators step beyond prescribed roles embedded in the charity paradigm of service-learning to invite and support egalitarian community and equity-driven decision-making from a project’s inception and development, through its unfolding and its assessment? Second, the sheer fact of a project taking place in the marginal place between two contexts gives all participants—students, faculty, community participants and hosts—the opportunity for meta-reflection on the institutional logics that construct and constrain our perspectives so acutely. What can we do, by way of project-conception and pedagogy, to open up those insights? The vantage that “the space between” provides can bring fresh understanding of the systemic forces at work in the lives of the community participants. And the university’s assumptions about itself and its place in the world can also suddenly appear strange and new, objects of scrutiny for students and community members both.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.085 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".