Cross-Sector Social Partnerships for Social Change: The Roles of Non-Governmental Organizations
Bibliographic record
Abstract
Complex social and environmental issues call for broader collaboration across different sectors so as to instigate transformative social change. While previous scholars have emphasized the role of non-governmental organizations (NGOs) in facilitating social change, they have not provided a nuanced assessment of NGOs’ different roles. We use the Poverty and Employment Precarity in Southern Ontario (PEPSO) research partnership as a study case and explore NGO partners’ different roles in a large cross-sector social partnership (CSSP). By interviewing 12 NGO partners and 4 non-NGO partners involved in the PEPSO research partnership, our research results show that NGOs primarily have 10 roles in a CSSP. They include enabling roles such as consultant, capacity builder, analyst, and funder; coordinating roles such as broker and communicator; and facilitating roles such as initiator, leader, advocate, and monitor. These roles allow NGOs to fulfil their duties to make substantial contributions to a CSSP.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".