Understanding School Board Leaders Use of Online Resources to Inform Decision-Making | Examen de l’usage des ressources en ligne par les dirigeants des conseils scolaires pour guider les prises de décisions
Bibliographic record
Abstract
In the past five years, there has been considerable interest in the decision-making process of school board officials in the field of education. However, a paucity of research exists on how these leaders use online resources to make decisions. Through an online survey and face to face interviews, this study examined the use of online resources by school-board trustees (n=164) to guide board-level decisions. Trustees used online articles (news, research articles, journals) twice as much as social media (Twitter, Facebook, blogs) or repository services (clipping services, Google Scholar). Almost 70% of trustees used three or more resources to inform their decision making. Seventy-five to 85% of trustees rated online articles and repository services as being useful. Trustees actively checked the trustworthiness of online resources by evaluating sources, cross-checking data, and asking colleagues. Key barriers to using online resources included lack of time, finding reliable or relevant information, and negotiating conflicting results. Some trustees wanted access to a third-party, repository of valid, reliable information.Au cours des cinq dernières années, le processus de prise de décisions des officiels des conseils scolaires a suscité un grand intérêt. Il existe cependant peu d’études sur la façon dont ces dirigeants utilisent les ressources en ligne pour guider leurs prises de décisions. Grâce à un sondage en ligne et à des entrevues menées en personne, la présente étude se penche sur l’usage que font les commissaires scolaires (n=164) des ressources en ligne pour appuyer les décisions du conseil. Les commissaires se servaient d’articles en ligne (actualités, articles de recherche, revues) deux fois plus que des réseaux sociaux (Twitter, Facebook, blogues) ou de services d’archivage (services de coupures de presse, Google Scholar). Près de 70 % des commissaires se servaient de trois ressources ou plus pour guider leurs décisions. De 75 % à 85 % des commissaires estimaient que les articles en ligne et les services d’archivage étaient utiles. Les commissaires vérifiaient activement la fiabilité des ressources en ligne en évaluant les sources, en recoupant les données et en demandant l’avis de collègues. Les principaux obstacles à l’usage des ressources en ligne comprenaient le manque de temps, la difficulté à trouver des renseignements fiables ou pertinents, et l’évaluation de résultats contradictoires. Certains commissaires souhaitaient accéder à des archives externes rassemblant des renseignements fiables et valides.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".