Extracting Prototypes From Lexical Feature Norms for Settlement Concepts
Bibliographic record
Abstract
The present study explored whether people share a common understanding of different settlement concepts despite individual variation. Participants completed a property listing task where they were asked to generate features for 57 settlement concepts. Hierarchical cluster analysis identified distinct clusters based on shared features. Central tendencies extracted from clusters at different levels of abstraction revealed featural prototypes and an overall family resemblance structure. To probe the effects of regional context on conceptual structure, subsequent cluster analyses used a subset of participants who were long-term residents of Canada or the United States. Prototypical features varied regionally, suggesting an effect of geographical region on conceptual structure. However, the results should be interpreted cautiously, as more data are needed to understand such differences in representation. Findings centralize the utility of semantic feature norms in understanding how people collectively think about where they live, and the importance of context effects on representations of settlements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".