Indigenous women in educational leadership: identifying supportive contexts in Mi’kmaw Kina’matnewey
Bibliographic record
Abstract
This article is drawn from a larger qualitative case study that examined the leadership context and leadership approaches of five Mi’kmaw women school principals in Mi’kmaw Kina’matnewey (MK), an Aboriginal educational authority, located in Nova Scotia, Canada. This article aims to identify the contextual supports within MK that have enabled Mi’kmaw women educators to obtain and retain positions as principals. The use of a decolonizing methodology positioned the participants to work in partnership with the researcher during data collection and analysis. Data collection and analysis involved the use of one-on-one and sharing circle conversations with the principals. Findings suggest that the social, cultural, and organizational contexts where women lead have had a significant influence on their lives. More specifically, familial, collegial, community, and organizational supports have enabled these women to hold positions as principals and enabled Mi’kmaw cultural revitalization to occur within their school communities. Although the contexts within MK are not reproducible, aspects of the supports within these contexts can be employed by schools and school districts to support the hiring and retention of minoritized members of society in educational leadership.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".