Educational Administrators’ Perspectives of Democracy and Citizenship Education: Interviews with Educational Leaders.
Bibliographic record
Abstract
Little is known about public school educational administrators’ perspectives of democracy and citizenship education and how those perspectives shape the learning that occurs in the schools they lead. This paper presents findings of a qualitative study that used semi-structured interviews of public school educational administrators’ perspectives of democracy and citizenship education in the province of Alberta, Canada. Four participants’ detailed responses were analyzed using an interpretive phenomenological methodology and coded into four themes. While all four participants felt that democratic and citizenship education were important, their conceptualizations varied widely and only one participant was found to lead in a way that encouraged democratically desirable education. Findings suggest that some educational administrators do not necessarily understand their role or responsibility in the education of democracy and citizenship within the schools they lead. Moreover, this study suggests that factors that hinder democratic and citizenship education are: school administrators’ preference to remain obedient to a top-down approach of school management; resource taxing administrative obligations and; a misunderstanding of ‘thick’ democracy. Factors that were found to facilitate democratic and citizenship education include: physical school and learning program design and; democratic school 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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".