Bibliographic record
Abstract
The COVID-19 pandemic has brought to light the importance of hindsight in response to global health crises. Although globalization has amplified worldwide perspectives, many lessons learned from past outbreaks in Indigenous communities have been overlooked. Oral histories are deeply rooted traditions that have played a significant role in the health practices of Indigenous communities across Canada. These practices can provide valuable insights into past epidemics or casualty events and their short- to long-term impacts. They have shaped responses to COVID-19, with Indigenous communities implementing self-determination efforts, such as community closures, contact tracing, and isolation measures. These traditions have heavily influenced population health practices in other contexts, such as the 1700 Cascadia earthquake, smallpox, and tuberculosis outbreaks. However, challenges remain in facilitating disease data transparency and Indigenous sovereignty. Efforts should be made to promote recognizing and respecting Indigenous knowledge and practices within the broader health system.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".