What is happening where? An evaluation of social sciences research trends in Nunavut (2004–2019)
Bibliographic record
Abstract
Research licensing administered by the Nunavut Research Institute (NRI) affords Nunavummiut (people of Nunavut) an opportunity to engage in research. The NRI partnered with researchers at McMaster and Carleton Universities to investigate social sciences research licensed between 2004 and 2019. This partnership aimed to understand the scope of research trends in Nunavut. Thematic content analysis was used to (i) identify research topics in social sciences and Inuit knowledge projects; (ii) determine frequency and diversity of topics according to leadership, location, and timeframe; (iii) develop metrics to improve tracking of research; and (iv) contribute to the development of a Nunavut research portal making NRI research applications and reports public. Social sciences research increased during the 16-year study period. Projects were predominantly led by Canadian academics. The highest intensity of research occurred in Iqaluit, and the lowest intensity in Grise Fiord. Social sciences research was mainly focused on topics related to Inuit culture and knowledge. Social scientists most often conducted research using interviews and shared their work via peer-reviewed journal articles. This project is a starting point in raising awareness about research trends for Nunavummiut. This work aims to contribute to broader efforts in developing Nunavut-specific approaches to achieving Inuit self-determination in research.
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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 | 0.071 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".