Geo-ethnography: Coupling Geographic Information Analysis Techniques with Ethnographic Methods in Urban Research
Bibliographic record
Abstract
This research article focuses on the coupling of geographic information system (GIS) technologies with ethnographic data, an approach we refer to as geo-ethnography. The data used here were gathered in an ongoing, multi-site study of low-income families and their children. Throughout our work, the goals have been to think creatively about how GIS can be used in welfare research, to stretch the technology, and to revise the methodologies we currently use. We specifically discuss the ways in which the ethnographic data on families and neighbourhoods have been integrated within a GIS and how these two methods, alone and in combination, help situate families’ actions and experiences in time and space and enhance data analysis and interpretation. More specifically, we focus on conceptual and methodological issues we have faced in the process of this integration and on practical strategies for combining qualitative and quantitative research.
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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 | 0.104 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".