Perspectives about living on the horns of dilemmas: An analysis of gender factors related to superintendent decision-making and problem-solving
Bibliographic record
Abstract
The major focus of this paper is a gender-based analysis of school superintendent decision-making and problem-solving as well as an investigation of contemporary leadership dilemmas. The findings are based on responses from 258 superintendents of K-12 school districts in Delaware, Maryland, New Jersey, New York, and Pennsylvania collected over a period of three years (2009-2011). The researchers also conducted 18 comprehensive qualitative “face-to-face” interviews with self-selected superintendents who responded to the quantitative survey. The intended outcome of this article is for education policy makers, professors, and practitioners to comprehensively examine the extent and degree of various dilemmas confronting the Mid-Atlantic Region school superintendent sample and to evaluate the decision-making and problem-solving approaches used by them. The study results that are presented will serve as valuable references to not only individual superintendents but also to university administrator preparation professors and to state administrator licensure agencies because it is important for all aspiring superintendents to know the various issues associated with education leadership and the personal and professional dilemmas that they need to be prepared to face as they embark on a career to improve schooling in the United States.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".