(Human) rights-based community practice for impactful communities – academic partnerships: A case study of the ICAN Master of Social Work Fellowship Program.
Bibliographic record
Abstract
Rights-based community practice-oriented international educational fellowships in a Master of Social Work that have led to impactful community-academic partnerships in military occupation and prolonged conflict zones are scarce.In this study, 18 semi-structured interviews (11 ICAN fellows and 7 academic leaders) are thematically analyzed, along with 128 documents, within a descriptive multi-site case study in order to examine the collective three-level impact of the 25-year span of the McGill International Community Advocacy Network's social work fellowship program on the level of fellows, academic institutions, and communities.Study findings align with contingency theory propositions, and point out that the integration of community-academic partnerships within academic institutions depends on the alignment of internal institutional, and external environmental factors that lead academic institutions to integrate these partnerships once fully aligned.Implications for this study entail future social work research, policy, education, and recommendations for peacebuilding.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".