Our practice of outreach during the Ice Monitoring project in Nunavik: an early-career researcher perspective
Bibliographic record
Abstract
Inuit Nunangat, including Nunavik, is seeing an ever-increasing number of research projects. While mainstream approaches to research are colonial in nature and have historically contributed to the oppression of Indigenous peoples, a new paradigm is now emerging from Indigenous recommendations. Researchers are encouraged to collaborate with Inuit or Northern communities, organizations, and governments and to develop communication strategies to keep local populations informed. This paper focuses on outreach activities organized on several occasions throughout the Ice Monitoring project, in which we participated as PhD students. We share details on this periodic outreach program, which included a Facebook page, hosting an information table at the Co-op store, activities with high school classes, and participation in Raglan Mine’s Environmental Forum. We also discuss lessons learned and the transformation of our practice.
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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 | 0.044 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.055 | 0.040 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".