Decolonizing Academic Funding: An Evaluation of an Indigenous Collaborative Funding Initiative
Bibliographic record
Abstract
In 2020 an innovative Indigenous community-led internal funding initiative was undertaken at a major university in Canada, followed in 2022 by a qualitative study of its inaugural round. This funding initiative is unique in that each research project was first identified by an Indigenous community/organization through a Canada-wide outreach process. The outreach process involved reaching out to over 700 Indigenous communities and organizations, via email or telephone, to ask if they would be interested in participating in a funded research project they identified as important and beneficial. The role of the university academic research partners was that of mentor rather than principal investigator for each project. Nine community-led research projects were funded and are currently underway. In this paper the authors outline the process of developing the initiative, its components, and findings from the study. Successes and challenges of the funding model are identified followed by a discussion of emergent themes. One of the key themes that emerged is that although there are aspects of this initiative that met its intended goals, it highlights a barrier in meeting one of the objectives, contributing to Indigenous research governance and self-determination.
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 | 0.142 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".