British Columbia School Trustees' Use of Research and Information Seeking in Decision Making.
Bibliographic record
Abstract
This replication-extension study (Earley & Galluzzo, 2015) examined the information seeking activities of British Columbia (BC) school trustees in an effort to understand the transmission of research. Trustees were asked to identify the sources they used for acquiring research and information in the process of decision making. The frequency of use believed most useful, and characteristics of information sources were examined as well as the influence of demographic and school district variables. One hundred and forty school trustees participated in this study. The most frequently used sources of information were briefing materials from the secretary-treasurer, briefing materials from the superintendent, and members of the local school board. The source of information believed most useful was briefing materials from the superintendent. There were no differences between genders on most items, although females were more likely to consult with the community than their male colleagues. Trustees in smaller districts were more likely to turn to materials from provincial organizations and the Ministry of Education website whereas trustees in larger districts tended to turn to local and provincial newspapers. Research reports from university researchers or think tanks were not primary sources of information for BC school trustees who generally sought information that was in close proximity from their school board and community.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".