Adapting to the Machine: Integrating GIS into Qualitative Research
Bibliographic record
Abstract
Geographic information systems (GIS) represent a technology developed over the past 30 years to facilitate the storage of spatial data and the solution of spatial problems. Recently, encouraging work in geography has begun to show the power of this technology for studying diverse social issues at a variety of scales. In this paper we demonstrate how social and spatial linkages might be effectively illustrated using GIS in qualitative, action-oriented research. In particular we focus on a component of the discipline that has traditionally not used GIS technology: feminist, community-based action research. We do this through a hypothetical dialogue between two geographers: a researcher with expertise in GIS and GIScience, and a researcher using feminist participatory and case-study methods who is interested in incorporating GIS into her studies. The dialogue will illustrate how a research strategy that combines GIS and qualitative methods might be advanced, using a specific study as the focus or the discussion.
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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 | 0.184 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".