‘There's only so much money hot dog sales can bring in’: The intersection of green school grounds and socio-economic status
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it