Educational Leadership at Moose Meadow School: A Contextualized Portrait of a Northern Canadian School and its Principal
Bibliographic record
Abstract
Research studies of northern Canadian schools are rare (Goddard and Foster, 2002). From this point of departure, this article presents a portrait of Moose Meadow School and its non-Indigenous principal, employing A. Richard King’s The School at Mopass: A Problem of Identity (1967) as a historical backdrop. In light of the paucity of northern educational research, this article presents two descriptions: the school and the principal’s daily life as operated under the central authority of the Yukon Department of Education in 2008, and that of a residential school in the 1960s, as operated by the federal government department then known as Western Region, Indian Affairs Branch, Canadian Department of Citizenship and Immigration. Observations and interviews were employed as data gathering instruments to record the daily operation of the school and the actions of the school principal. The article offers a theoretical contribution with respect to the role of identity and constructions and enactments of educational leadership. It further sheds light on the centrality of the school in northern rural communities where Aboriginal land claims have been settled with territorial and federal governments.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".