Bibliographic record
Abstract
At the time that we conducted this workshop and published the workshop report, we were known as the Ajunnginiq Centre.Several months later, in October 2008, the Ajunnginiq Centre changed its name to Inuit Tuttarvingat.In order to keep the name on our documents consistent, we have re-published this workshop report under our new name -Inuit Tuttarvingat. Inuit Tuttarvingat (formerly known as the Ajunnginiq Centre)Inuit Tuttarvingat of the National Aboriginal Health Organization shall promote practices that will restore a healthy Inuit lifestyle and improve the health status of Inuit, through research and research dissemination, education and awareness, human resource development, and sharing information on Inuit-specific health policies and practices.Inuit Tuttarvingat's five main areas of focus are to:Improve and promote Inuit health through knowledge-based activities; • Promote understanding of the health issues affecting Inuit; • Facilitate and promote research and develop research partnerships; • Foster participation of Inuit in the delivery of health care; and, • Affirm and protect Inuit traditional healing practices.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".