Vice Principalship and Moral Literacy: Developing a Moral Compass
Bibliographic record
Abstract
Competency in moral Literacy, like any other literacy develops through careful and continual practice (Herman, 2007). In this qualitative study we explore the vice principalship and the development of administrative moral literacy. Using a northern Ontario Canada case study, we recount how three secondary school vice principals further their own moral literacy through the execution of their professional role—specifically, how such practices as professional decision making, self-reflection and the use of personal self messaging can ameliorate moral literacy competency. We used both interviews and job-shadowing to investigate how participants navigated the challenges of the vice principalship, and how participants defined and measured success. We analysed stories and metaphors (Clandinin and Connelly, 2000; Seidman, 2006) to identify specific skills and strategies in the formation of moral literacy, such as a sense of moral purpose, self-knowledge and self-regulation, flexibility, vision and a high tolerance for ambiguity.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".