Doom and gloom or a time for optimism: Potential aspirants' views about school leadership - now and for the future
Bibliographic record
Abstract
Principal recruitment has attracted national and international attention in recent years (eg. Barty et al, 2005 in Australia; Earley et al, 2002 in the UK; Brooking et al, 2003 in New Zealand; Williams, 2003 in Canada). Importantly, Australian research in both state and non-state schools suggests that potential principal aspirants are less enthusiastic than might be expected in their desire to become principals (D’Arbon et al, 2002; Cranston et al, 2004; Lacey, 2002). Given the importance of ensuring we have quality leaders for our schools in the future, the research reported here (which is on-going) examined the views of potential aspirants (primary and secondary deputy principals) from one large government education system in Australia about the principalship and their intentions in seeking promotion (or otherwise) to such positions and the reasons driving these intentions. Data were collected via the Aspiring Principals Questionnaire (APQ) – especially developed for the study – comprising 38 closed items mainly of a Likert-type format, 5 open-ended items linked to particular closed items allowing participants to add their own suggestions/ideas, expand/elaborate on responses; and 4 further more general open-ended items. A number of system-level policy and practice recommendations have been developed from the findings.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".