Deficit-Based Indigenous Health Research and the Stereotyping of Indigenous Peoples
Bibliographic record
Abstract
Health research tends to be deficit-based by nature; as researchers we typically quantify or qualify absence of health markers or presence of illness. This can create a narrative with far reaching effects for communities already subject to stigmatization. In the context of Indigenous health research, a deficit-based discourse has the potential to contribute to stereotyping and marginalization of Indigenous Peoples in wider society. This is especially true when researchers fail to explore the roots of health deficits, namely colonization, Westernization, and intergenerational trauma, risking conflation of complex health challenges with inherent Indigenous characteristics. In this paper we explore the incompatibility of deficit-based research with principles from several ethical frameworks including the Tri-Council Policy Statement (TCPS2) Chapter 9, OCAP® (ownership, control, access, possession), Inuit Tapiriit Kanatami National Inuit Strategy on Research, and Canadian Coalition for Global Health Research (CCGHR) Principles for Global Health Research. Additionally we draw upon cases of deficit-based research and stereotyping in healthcare, in order to identify how this relates to epistemic injustice and explore alternative approaches.
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 | 0.074 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.099 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".