Bibliographic record
Abstract
Abstract Public education in Alberta is undergoing substantive change and there is renewed interest in how school superintendents make decisions. My inquiry came from a practitioner’s perspective looking into superintendent’s decision-making processes. Eight serving school superintendents were interviewed to determine the influences on their decision-making around governance, human resource and accountability issues. I sought insights to inform superintendent practice in the province and uncover further questions for study. The research question used to identify the expectations, influences and understandings of public school superintendents regarding decision-making within their respective school jurisdictions was: What factors impact decisions related to jurisdiction governance, human resource management and accountability in the superintendency? A multiple case-study model was utilized to review responses from the purposive sample. The sample was balanced for gender and geographic and demographic diversity. Transcripts, government documents and research journals were utilized in the analysis as understandings were revealed and explanations built in response to the research question. The effect of time, role identification, relationship building, capacity building, and community expectations were identified as common factors affecting the decisions of school superintendents. Roles and responsibilities within school jurisdictions and whether an authoritative or participative approach to decision-making was utilized varied across genders and jurisdiction size and location. Perceived self-efficacy of superintendents in their role and perceived organizational efficacy of school jurisdictions in the public education system emerged as influences on the process. Superintendents indicated a clear preference for processes rendering decisions from understanding rather than decisions designed to compel understanding. Responses from superintendents in this study indicated they valued a collaborative approach to decision-making and a desire to transform decision-making from a process focused on individual roles and responsibilities to one supporting broader stakeholder values. Participants sought decisions that ultimately met the academic, social and emotional needs of the students. Changes to the landscape of public education in Alberta created by a new Education Act (2012) and the evolving expectations of society will require superintendents to make critical decisions in the months to come. The findings of this study support them in that work.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".