Measuring school facility conditions: an illustration of the importance of purpose
Bibliographic record
Abstract
Purpose The purpose of this paper is to argue that taking the educational purposes of schools into account is central to understanding the place and importance of facilities to learning outcomes. The paper begins by observing that the research literature connecting facility conditions to student outcomes is mixed. A closer examination of this literature suggests that when school facilities are measured from an engineering perspective, little connection to learning outcomes is evident. By contrast, when school facilities are rated in terms of educational functions, a connection to learning outcomes is apparent. Design/methodology/approach The paper provides an empirical test of the educational relevance of how school facilities are measured. Using the schools in a Canadian division, the condition of school facilities was measured in two ways, including both conventional, engineering tools and a survey capturing principals' assessments. School facility ratings using these alternate measurement methods were correlated with schools' quality of teaching and learning environments (QTLE). Findings Two central findings emerge. First, engineering assessments of facilities are unrelated to the QTLE in schools. Second, educators' assessments of school facilities are systematically related to the QTLE in schools. Originality/value The findings indicate that more research needs to be directed at developing sound tools for measuring school facilities in terms of their educational relevance. In addition, school administrators need to reconsider policies that devalue the contribution that facilities make to learning outcomes.
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 | 0.032 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".