Bibliographic record
Abstract
Purpose The purpose of this end piece, framed in aesthetic and critical theory, is to review the author's own approach with graduate students regarding the omnipresence and significance of emotion in organizational leadership, and to comment on the contributions to emotional theory found in this volume of the Journal of Educational Administration. The objective in the author's own research is to assist students, through aesthetic awareness, in moving beyond “one‐dimensional” thinking and the “iron cages” of organizational experience. Design/methodology/approach As personal affect and perspectives of meaning were of primary importance in the author's research, she employed participatory action research methods. Qualitative data were drawn from students over a ten‐year period as they responded to a question about connections between aesthetic presentations – given by their colleagues as short introductions to each class – and organizational life as they experienced it personally and theoretically. Findings Aesthetics, understood not only as appreciation, but also as action, brings to students the illuminating power of multiple forms of expression. Through expression, that is, students named feeling, affect, and begin to understand the nature of emotion. The arts provide ways of expression apart from, and including, the spoken word. Originality/value The arts, and an understanding of aesthetics, opens a rarely travelled route whereupon students may engage in organizational theory as a humane science.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".