Bibliographic record
Abstract
School administrators are often seen as the middle managers who are held accountable not only to their schools and communities but also to their employers. In order to successfully carry out their duties and responsibilities, school administrators must feel psychologically safe to speak up or speak out, ask a difficult question, voice an opinion, express a dissent, talk about a mistake, stand out for a position, or take a risk at work without fear of negative consequences. Psychologically precarious situations can compromise school administrators' ability to think, feel, speak, and act, and ultimately impact their leadership, performance, and commitment to their work. Regrettably, school administrators' psychological safety has been overlooked over the years. This research provides much-needed insights into their psychological safety in navigating this dual reality between schools and districts. The survey research garnered data from public school administrators in British Columbia, Canada, and explored school administrators' perceptions of psychological safety at their schools and districts and its manifestation across different demographics. The multiple logit regression models show that school administrators felt less psychologically safe in their district than at their schools. The research evidence points to the importance of fostering a psychologically safe and healthy work environment in which school administrators feel psychologically safe to say: "The emperor has no clothes".
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.008 |
| 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".