The Problem of the Penumbra: Elementary School Principals' Exercise of Discretion in Student Disciplinary Issues
Bibliographic record
Abstract
Maintaining order and discipline is one of the most challenging aspects of the school-based administrator’s role. School officials make disciplinary decisions within a context that is established, in part, by case law, legislation and regulations, school board policies and social, organizational and individual values. The exercise of administrative discretion is vital to the decision-making process. It offers school leaders creativity and flexibility. The interpretation and implementation of school discipline policies by in-school administrators can provide insight into their discretionary decision-making. The purpose of this study was to determine how principals in an urban school division in Western Canada negotiated within the legal parameters of discretion as it is delegated to them in legislation and school board policy in order to be faithful to their own values system in matters of student discipline. The study assumed Christopher Hodgkinson’s hierarchy of values as a theoretical framework for examining the administrators’ decision-making, and used H.L.A. Hart’s concept of law as a system of rules as an additional lens through which to view the exercise of administrative discretion. Employing an interpretive qualitative research methodology, the researcher conducted in-depth, semi-structured interviews with ten elementary school principals. Findings of the research reflect that the way principals perceived the exercise of discretion enabled them to maintain school safety, to be, in their judgment, fair and just in decision-making in disciplinary situations, to balance competing rights in the school setting and to make decisions in what they understood to be the best interests of their students. The principals’ exercise of discretion, however, appeared to be subject to various values and influences which could lead to injustice and arbitrariness in decision-making. In the end, I concluded discretionary power should be structured, limited and subject to review in order to provide accountability to stakeholders. Implications of the study suggest principals should develop greater awareness of their own values system and be reflective about their judgments and decision-making. Schools should have clearly-defined codes of conduct, and school divisions should outline expectations for discipline policy implementation and adherence by principals.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.045 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| 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".