Teaching Principals in Small Rural Schools: “My Cup Overfloweth”
Bibliographic record
Abstract
This paper presents the results of interviews with 12 Manitoba and Alberta rural teaching principals regarding their leadership practices in small schools. The overwhelming theme mentioned by these teaching principals was the joy and sense of purpose they found in the relationships they cultivated with children, staff, and community members because of the ‘advantages’ they had working in small schools. The paper details the small schools context within which teaching principals are working in these two provinces and outlines the role of reciprocal relationality that is central to their leadership efforts in small rural schools. Cet article présente les résultats d’entrevues auprès de douze directeurs-enseignants d’écoles rurales au Manitoba et en Alberta portant sur les pratiques de leadership dans les petites écoles. Le thème dominant qui en est ressorti est celui de la joie et le sentiment d’un but à atteindre qu’ils retiraient des rapports entretenus avec les enfants, le personnel et les membres de la communauté et qu’ils associaient aux « bienfaits » de travailler dans une petite école. L’article décrit en détail le contexte scolaire dans lequel travaillent les directeurs-enseignants dans ces deux provinces, et dresse un portrait du rôle de la relationnalité réciproque qui est au centre de leurs efforts comme dirigeants de petites écoles en milieu rural.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".