Cultural Greenspaces: Synthesizing Knowledge and Experience in Nova Scotia’s African-Canadian Communities through Participatory Research and SoftGIS
Bibliographic record
Abstract
Greenspaces are integral components of communities and provide numerous benefits. However, human development threatens these spaces, particularly in communities of color where histories of racial injustice persist and environmental vulnerabilities remain. A step towards preventing the loss of important cultural greenspaces is documenting knowledge and experience. This research employed community-based participatory techniques to study the relationship between the landscape and African-Canadian communities around Preston, Nova Scotia, the oldest and largest in Canada. Community-directed meetings created collaborative-based knowledge about perceptions surrounding land use change while identifying valued greenspaces. This paper studies the relationships between the community’s greenspaces and the benefits to psychological, social, and physical aspects of human wellbeing. This relationship is operationalized through the use of a public participation geographic information system (PPGIS), SoftGIS, which activates the greenspace–human wellbeing relationship through interaction and its map-based survey data collection. Results indicate residents predominately visited greenspaces near a church or community center for social wellbeing benefits to interact with neighbors and friends, to cookout, or to bring children outside. This research contributes to a greater understanding of the Preston area’s greenspace identification and qualification, resident behavior, and cultural perspectives to inform strategies and goals for engaging government agencies surrounding policy and land use planning. This research illustrates frameworks for improving building capacity and promoting racial equity within the urbanization process in other communities.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".