Inaakonigeng ige-zhiwebiki’ba: Self-determining our path on the future of Indigenous STBBI research with the Feast Centre
Bibliographic record
Abstract
The future of Indigenous STBBI (sexually transmitted and blood-borne infections) research must address the unique needs of diverse First Nations, Inuit, and Métis (FNIM) communities across Canada. This requires the expansion of culturally responsive research approaches centred on FNIM ways of being, knowing, and doing. The Feast Centre for Indigenous STBBI Research (Feast Centre) is dedicated to expanding the use of FNIM research methods in response to the unique needs of distinct Indigenous communities and foregrounds the voices of Indigenous Peoples living with or affected by STBBI. Indigenous Peoples in Canada experience higher rates of STBBI compared to other populations, and this is linked to significant health disparities, meaning that conventional public health approaches are not meeting the needs of Indigenous communities. Canada’s colonial health policies sustain health disparities through a lack of culturally responsive approaches to STBBI prevention, treatment, and care. In this article we examine Indigenous STBBI initiatives foundational to the Feast Centre by focusing on the outcomes of a CAAN Communities, Alliances & Networks–led national Indigenous community consultation, the findings of the project’s Indigenous HIV and AIDS scoping review, and vital theoretical insights from Indigenous STBBI literature. We provide key recommendations that emphasize culturally responsive approaches to STBBI research that strive to meet community-identified needs while cultivating the inherent strengths of FNIM communities. We envision these key recommendations within the theoretical framework of Indigenous futurisms in ways that reconceptualize Indigenous STBBI research through cultural strengths and offer guidance for the direction of future 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 | 0.051 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".