Bibliographic record
Abstract
Welcome to issue 15(2) of the Canadian Journal of Nonprofit and Social Economy Research (CJNSER).In this issue, we feature four research articles, three contributions to the "Perspectives from the Field" section, and two book reviews.Before we focus on describing the contents, we would like to thank, as always, all those who contributed to the success of this issue-first and foremost, the authors and reviewers, the editorial board, and the technical support staff.The work of all these people is fundamental.We are also excited to announce that we have been accepted to the Érudit platform (erudit.org), a pan-Canadian interuniversity consortium consisting of Université de Montréal, Université Laval, and Université du Québec à Montréal.We anticipate that our presence on this platform will expand even more our reach to francophone authors and readers.The first three scientific articles of this issue deal with community-based organizations and civic engagement from different perspectives and contexts.The first does so by studying civic and community engagement, the second by examining the nonprofit commu- CJNSER / ReCROES
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.354 | 0.196 |
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".