Next Generation of knowledge partnerships for global development. Introduction
Bibliographic record
Abstract
Despite the rich potential benefits to be had from collaborations amongst practitioners and academic communities in the Canadian global development field, there is a general sense that such exchanges happen much less frequently than they could. The Next Generation programme, which underpins this special issue, presented an opportunity to address knowledge gaps in the current ecosystem of academic-civil society organization (CSO) collaborations, producing new research presented in this issue. Between 2016 and 2019, the NextGen Program sought to test and foster different ways and models of facilitating cross-sectoral collaboration between academics and CSOs in Canada. This introduction takes a reflexive approach, including with respect to the Program’s partnership between the Canadian Association for the Study of International Development (CASID) and Cooperation Canada (formerly known as the Canadian Council for International Cooperation (CCIC)), to present some key lessons and findings from cross-sectoral collaborations in the global development sector. The article then draws on the experiences of a wide range of collaborative models to draw some conclusions about how to nurture a conducive knowledge partnership ecosystem looking forward.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".