Immigrant Engagement in Public Open Space: Strategies for the New Boston
Bibliographic record
Abstract
Today, almost 30% of Boston residents were born outside the United States and of these, nearly half came from Latin America, a quarter from Asia, and almost 10% from Africa. The future of the city's open space system - how much land is set aside, and how that land is designed, maintained, and used - will increasingly depend on the passion and commitment of families and communities who may not see themselves or their interests reflected in the city's public lands.In this paper, we consider some of the ways in which recent immigrants to Boston connect (and do not connect) to public parks and open spaces. Our goal is two-fold: to explore alternative ways of "seeing" and using parks and open spaces in different communities in the city, as well as to highlight specific strategies, both here and across the country, that successfully engage urban residents born outside the United States. If Boston's civic spaces are to be celebrated in the future as they have been in the past, they must come to reflect the new diversity of Boston's people. Our hope is that these stories and models will encourage more culturally resonant uses of parks and other public open spaces, and equip policy makers and environmental organizations to partner more fully with newcomer communities - in Boston and beyond.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".