Bibliographic record
Abstract
Canadian Indigenous research in the past had had a history of deception. However, the European settlers later acknowledged the injustice committed in research and now actively engaged and respect their perspectives. Miawpukek First Nation (MFN) community in Newfoundland and Labrador is considered one of Canada's most well-administered Indigenous communities due to strong leadership and pragmatism in every developmental activities, including research. MFN has integrated indigenous perspectives in the health and wellbeing of the community. In the health care center, they did not put indigenous medicines into the modern-day clinic. They did not discourage it, but indigenous medicines were done through focusing on the land with hunting and gathering to make these indigenous medicines. There are many different kinds of health care. The Indigenous people prefer to walk through the woods to gather natural medicines. The community has integrated lighted walking trails, a gym, weight rooms and a community garden to help with their well-being and mental health. They are trappers and hunters and live off the land. Therefore, prefer this way of life over modern medicine when able. The climate change and planetary health crisis in the world is affecting everyone and everything, lands, oceans and animal migration are all being affected. The Indigenous communities used to do their fishing in the winter when the ice froze but because of the climate change there is no ice to fish on. The animals moved further south, for example, the polar bears to find food as well the fish farms are dying off. Everyone must play their part and work more towards green energy.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".