Moving educational leaders from implicit to explicit leadership: an action research study
Bibliographic record
Abstract
This study examines whether senior educational leaders from publicly funded school boards, colleges and universities in Ontario make decisions with primarily implicit or explicit models of leadership; and whether implicit leaders might find an explicit framework useful, such as one developed by the researcher (SELF—Success-Evoking Leadership Framework) or one that they construct. Using a customized survey, most leaders indicated they do not rely on explicit models of leadership. They were asked about their experience with explicit leadership models and whether they would agree to be interviewed, as a potential prelude to participating in the action research component—two additional interviews, four telephone conversations, daily e-mails and a daily log. Survey findings concluded that 82.1% of respondents were in their present position for less than seven years, and in educational leadership for eleven to twenty years. On leadership models, empowerment was used most (27.8%) with all the others being used equally (16.7–20.4%). Most used both an explicit and intuitive leadership model (39.9%), principally from experimentation, but 25% from graduate school. The basic analysis of the interviews was done with a word count. Of the leadership “keywords” selected by the researcher, principally from SELF, the most used were “values” and “learning” (eight times each). Many of the keywords unexpectedly appeared only once or twice, possibly indicating lack of a common vocabulary among senior educational leaders in Ontario. The researcher selected three primarily “intuitive” participants by balancing seven criteria. All met as scheduled, processed their e-mails and logs daily, and reflected on the relationship between their tasks and SELF, to which the researcher reflected in turn, with personal observation and experience. All concluded SELF had provided insight into their tasks and provided more “intention” regularly to their leadership, affirming for the researcher that an explicit leadership model can assist educational leaders in their mission, vision, values, culture, processes, decisions and actions. As a result of using SELF as a coaching vehicle, the researcher has amended SELF and recommended areas for further study.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".