Integrating culturally relevant learning in Nunavut high schools: student and educator perspectives from Pangnirtung, Nunavut, and Ottawa, Ontario
Bibliographic record
Abstract
The current emphasis in Nunavut high school education, and curriculum development, is to more effectively integrate culturally appropriate learning while also preparing students for their post-graduation goals. Working with students and educators in Pangnirtung, Nunavut and Ottawa, Ontario provided an opportunity to investigate how this goal is manifesting within and outside classroom activities, as well as how this supports student engagement, success, and pride in cultural identity. There are strong joint intentions and efforts being made by Inuit and Qallunaat (non-Inuit) educators alike to work together,involve community members, and bring Inuit Qaujimajatuqangit (IQ - Inuit ways of knowing, being and doing) principles into their school and/or classrooms. However, there are challenges with the practical implementation of an integrated learning approach, resulting in a disconnect between cultural and academic learning. Insights gained through this research aim to provide examples and recommendations to contribute towards ongoing efforts to make improvements for future generations of Inuit.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.044 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".