Bibliographic record
Abstract
Purpose The aim of the paper is to argue that principal preparation programs should help candidates: recognize the political role of the school principal; develop political skills (including the ability to strategically appropriate policy); and understand that the political approach of the principal influences teaching, learning, relationships, governance, and reform efforts. In addition, the paper reports findings of the analysis of Ontario's Principal Qualification Program guidelines to determine if they require principal preparation programs to develop aspiring school leaders’ political skills. Design/methodology/approach The paper reviews theoretical arguments and empirical studies from the fields of school micropolitics, business, educational leadership, and critical policy studies to establish five political skills principals require. The authors then conducted a content analysis of Ontario's Principal Qualification Program guidelines to determine if they require principal preparation programs to develop aspiring leaders’ political skills. Findings Ontario's Principal Qualification Program guidelines do not explicitly direct principal preparation programs to help candidates develop political skills. However, the guidelines recognize that principals pursue political goals and work in political environments, and they offer opportunities for appropriating the guidelines in ways that promote the development of principal candidates’ political skills. Originality/value The paper is the first to analyze Ontario's Principal Qualification Program guidelines to determine if they require principal preparation programs to develop aspiring leaders’ political skills. It also identifies policy appropriation as a political skill that should be developed in principal preparation programs and provides a model of how principal preparation policies themselves may be appropriated to support a focus on developing aspiring principals’ political skills.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".