Describing a First Nations-led grant program for COVID-19 Research: The APPRISE-Ramsay First Nations COVID-19 grant program
Bibliographic record
Abstract
We describe the establishment of a First Nations governed grant program that built on an existing Australian research collaboration during the height of the COVID-19 pandemic in 2020. Following a generous philanthropic donation, a process was initiated to centre and privilege First Nations perspectives and governance in the grant dissemination process. Decision-making was driven by First Nations people, including setting research principles and priorities, eligibility and review criteria, and in overseeing the advertising and grant review. This led to a widely distributed and highly competitive application round and the funding of ten grants from diverse organisations addressing various aspects of the COVID-19 response. The resulting grant outputs were diverse and impactful, including academic publications, articles for general readership, internal reports, social and traditional media, and frameworks. The principles from the grant round have underpinned the more recent formation of the ongoing First Nations Research preparednesS neTwork (FIRST), to further embed the important principles of First Nations self-determination for ongoing and future pandemic research.
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 | 0.058 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".