UNDERSTANDING PERSON-PLACE TRANSACTIONS IN NEIGHBOURHOODS: A QUALITATIVE-GEOSPATIAL APPROACH
Bibliographic record
Abstract
Emerging research regarding aging in context reveals much about neighbourhood characteristics that relate to aging adults’ health, participation and inclusion; however, in-depth information about the nature of person-place relationships is lacking. Shifting away from previous conceptualizations of place as static, a transactional perspective considers place as inseparable from the person, each shaping the other through complex, ongoing transactions. The inter-woven nature of person and place highlights the need to use methods that can examine this relationship in situ and explore meanings derived from places. Participatory geospatial methods (i.e. methods that involve the participant in collecting geospatial data) can capture situated details about place that are not verbalized during interviews or otherwise discerned, and qualitative methods can explore interpretations, both helping to generate deep understandings of the relationships between person and place. This presentation argues for applying qualitative-participatory geospatial approaches to this area of study and describes an innovative methodology. A study exploring how neighbourhood and person transact to shape a sense of social connectedness in older adults provided the basis from which we developed a combined qualitative-participatory geospatial methodology. Methods included global positioning system (GPS) tracking followed by map-based interviews, narrative interviews, and go-along interviews, with attention to integrating spatial and other forms of data during analysis. Findings indicate the unique understandings that each method contributes, the strengths and limitations of integrating geospatial with qualitative data, and the potential for this methodology to generate knowledge about person-place transactions that can inform practice, policy and research to promote older adults’ well-being.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".