Academic culture: a promising mediator of school leaders’ influence on student learning
Bibliographic record
Abstract
Purpose This study is a quantitative exploration of a new construct the authors label as “academic culture (AC).” Treating it as generalized latent variable composed of academic press (AP), disciplinary climate (DC), and teachers’ use of instructional time, the purpose of this paper is to explore the potential of this construct to be a key mediator of school leaders’ influence on student learning. The study is guided by three hypotheses. Design/methodology/approach Responses by 856 elementary teachers from 70 schools to an online survey measured the three components of AC along with school leadership (SL). Provincial tests of writing, reading, and math were used as measures of student achievement (SA). Social economic status (SES) was used as control variable for the study. Data were summarized using descriptive statistics and correlations were calculated among all variables. Analyses included intra-class correlation analysis, regression equations, confirmatory factor analysis, and structural equation modeling. Findings Evidence confirmed the study’s three hypotheses: first, AP, DC, and instructional time formed a general latent construct, AC; second, AC explained a significant proportion of the variance in SA, controlling for student SES; and third, AC was a significant mediator of SL’s influence on SA. Concepts and measures of academic optimism (AO) and AC are compared in the paper and implications for practice and future research are outlined. Originality/value This first study of AC explored the relationship between AC and SA. Although at least two AO studies have included measures of distributed leadership, minimal attention has been devoted to actually testing the claim that AO is amenable to the influence of explicit leadership practices (as distinct from enabling school structures) and is a powerful mediator of SL effects on student learning. Addressing this limitation of AO research to date, the present study included a well-developed measure of leadership practices and assessed the value of AC as a mediator of such practices.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".