Comparing thematic and search term-based coding in understanding sense of place in survey research
Bibliographic record
Abstract
Sense of place is a fundamental concept in human geography, yet challenging to measure given its intangibility and idiosyncrasy. Meanwhile, there are increasing opportunities for social scientists to utilize big data and automated approaches to data analysis, albeit with some wariness, but few researchers directly compare automated to manual analysis in the context of sense of place. This study applies two analytical approaches to a survey question on sense of place: semi-automatic search term analysis around semantic fields, and inductive thematic analysis. Results show high agreement between the approaches, with more tangible aspects of place (recreation) better correlated than more abstract concepts (appreciation). Variation mainly relates to the ability of inductive coding to address false negatives, implied meaning, or obscure search terms. This demonstrates the potential value of hybridizing to improve the accuracy of a search term-based approach, and overcome the limitations, such as subjectivities, of one analytical approach.
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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 | 0.078 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".