Toward a Reconsideration of Biography as an Instrument for Studying Leadership in Educational Administration
Bibliographic record
Abstract
The thesis of this paper is that does offer something valuable to educational administration that it currently lacks, and that biography ought to be reconsidered as a legitimate instrument for the study of educational leadership. Traditional research methodologies (questionnaire surveys, for example) normally associated with positivist approaches to the study of educational leadership remain theorists' predominant mode of inquiry. Such methods, however, do not pay sufficient attention to the role played by institutional contexts in the social construction of leaders and leadership systems. The difference between biography and social science also relates to the level of generality – a sort of micro/macro distinction. Educational leadership theorists, by training and inclination, look to the general, while biography deals with the particular . Biography can be moved beyond narration and storytelling to the construction of case studies to test or evaluate theories. And it can be argued that to understand a system, we need to look at leadership both close up and from a long view. Previous approaches to the study of educational leadership decontextualized not only the decisions but also the process involved in developing them. Biography can restore the wholeness of the entire act of leadership.
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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 | 0.060 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".