Values and Leadership: Theory Development, New Research, and an Agenda for the Future
Bibliographic record
Abstract
This article presents an updated account of values and valuation processes as they occur in school settings. A tradition of epistemological and philosophical debate, as well as the dominance of empiricist perspectives in educational administration, have tended to separate the consideration of values as influences on leadership practices from the usual organizational or social collective perspectives common to the field. More recently, however, powerful social forces such as globalization and the increasing diversity of our societies have stimulated increased academic productivity in this sector. A more balanced view of values as an influence on administration is emerging, which combines notions of the personal values manifested by individuals and the professional values of administration with the collective values manifested by groups, societies, and organizations. Discussion and inquiry have now extended beyond the usual expert opinion and academic debate of theorists and philosophers to include practitioners, empirical verifications of theory, and important new research findings. In this article key concepts from theory and a selection of findings from research are reviewed. The application of theory and research about values through reflective educational practice is discussed. Certain methodological problems associated with values research are examined, and the ground breaking work of several key contributors to the field is identified and considered. The article concludes with some speculations on an agenda for future theory-building and research in the values field.
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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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.016 | 0.038 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".