Teaching at the top of the world: An autobiographical inquiry
Bibliographic record
Abstract
After a brief discussion on the research methodology used, this narrative is divided into three main sections. Part 1 is the personal account of the author's arrival and subsequent experiences as a teacher, principal and community member in Ausuittuq, Nunavut. A detailed description of the community and full school program is offered. Part 2 explores the varied strategies and teaching methodologies implemented at Aqiatusuk School over the 4-year period. The reasons for choosing these particular strategies and interventions are discussed. Attention is given to the themes of language and culture, artistic expression, and school-community partnerships. The implications for successful teaching in this unique, cross-cultural environment are explored. An analysis of the school program within the Inuit cultural context is given throughout the chapters of Part 2. Part 3 serves as the “conclusions” section to the thesis. It summarizes some possible answers to the question of what factors promote student success in a small, K–12 Inuit community school. Some of the challenges faced by northern educators are also explored. (Abstract shortened by UMI.).
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| 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".