‘There's only so much money hot dog sales can bring in’: The intersection of green school grounds and socio-economic status
Bibliographic record
Abstract
In the interest of enhancing children's environments, many school grounds around the world are being ‘greened’ as asphalt and manicured grass are replaced with a diversity of elements and spaces, such as trees, shrubs, gardens, art, and gathering areas. Despite a growing body of research from a number of disciplines that is exploring the potential of these spaces, very little is known about how issues of socio-economic status (SES) influence school ground greening initiatives. In this paper, I explore what (if any) relationship exists between school ground greening and SES in a Canadian school board where approximately 20% of more than 500 schools have begun the greening process. A mixed methods approach was used: (1) 149 questionnaires were completed by administrators, teachers, and parents associated with 45 school ground greening initiatives; and (2) 21 follow-up interviews were conducted with administrators, teachers and parents at five greening projects across a range of SESs. Three significant, and arguably troubling, patterns emerged as a function of socio-economic status of the school community. Participants associated with schools across a range of SESs had different: (1) perceptions as to the importance/adequacy of green school grounds; (2) access to adult support; and (3) access to funding. The implications of these findings are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".