Bibliographic record
Abstract
Every school district in North America has a superintendent. But what occupies their time? That was the core question of this project. This study looked at how school superintendents in British Columbia, Canada, spent their time during the school year. This study explored the demographics of superintendents and their school districts, the level of responsibility that superintendents reported on various leadership and management tasks, the time spent in key areas of their work, and their perceptions of their ability to control their time and the impact of COVID-19 on their work.All superintendents in British Columbia were asked to complete a questionnaire for this study. Of the 60 superintendents, 59 participated (98%). This survey was conducted via email in the spring of 2021. Nine key themes were established through the research: • Superintendent gender has an impact • Student population size matters – At least until about 6,000 students • Some stunning numbers with experience, but little impact on their work • Superintendents are drawn into the urgent • Superintendents are committed to being educational leaders • Learning leader vs. community leader • Boards matter • Control of time is a matter of perspective • COVID-19 created complexity and opportunity BC School Districts have been lauded as some of the top performing in the world. This study showed the complexity of the work of their top leaders and the commitment of the superintendents of BC to be educational leaders for their students, staff, and community.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".