Teaching Principals in Rural, Remote, and Northern Schools in Canada: An Empirical Analysis of Workload, Roles, and Instructional Leadership
Bibliographic record
Abstract
This paper reports on findings of a study that examined the role of teaching principals in rural, remote and northern schools in Canada. A teaching principal is a principal who has a “double load” or dual roles in teaching and administration. The objectives of this study are: 1) to describe the role of the teaching principal in northern, rural, and remote school districts in Alberta, Saskatchewan and Manitoba; 2) to characterize the practices of teaching principals in rural, remote and northern school contexts in terms; and 3) to delineate implications of the above findings for leadership theory, practice and preparation. As part of a larger multi-methods study, we conducted a survey of 70 teaching principals in three Canadian provinces (Manitoba, Alberta and Saskatchewan) related to school and community contexts, workloads, and leadership, administrative and teaching responsibilities. This paper reports on the findings of the survey that demonstrate:1) the difficulties teaching principals face with respect to balancing administrative, teaching, and personal responsibilities; 2) belief that holding a teaching role while serving as a principal improves leadership capabilities; and 3) instructional leadership practices for teaching principals may be significantly different from those identified in the literature on instructional leadership.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".