Thinking Critically in Space: Toward a Mixed-Methods Geospatial Approach to Education Policy Analysis
Bibliographic record
Abstract
This paper suggests that synergies can be produced by using geospatial analyses as a bridge between traditional qualitative-quantitative distinctions in education research. While mapping tools have been effective for informing education policy studies, especially in terms of educational access and choice, they have also been underutilized and underdeveloped. This paper focuses on the potential benefits of expanding geospatial analysis, which has traditionally been heavily quantitative in its orientation, by incorporating qualitative research, including the accounts of lived experiences and perceptions that guide and shape institutional and individual behaviors and decisions. To that end, the paper proposes an agenda for mixed-methods research by drawing on new advances in the fields of human and critical geography.
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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 | 0.204 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".