Updating geographical knowledge: Principles of coherence and inertia.
Bibliographic record
Abstract
In 2 experiments, the authors investigated how representations of global geography are updated when people learn new location information about individual cities. Participants estimated the latitude of cities in North America (Experiment 1) and in the Old and New Worlds (Experiment 2). After making their first estimates, participants were given information about the latitudes of 2 cities and asked to make a second set of estimates. Both the first and second estimates revealed evidence for psychologically distinct geographical subregions that were coordinated, in an ordinal sense, across the Atlantic Ocean. Further, the second estimates were affected by the nature of the physical adjacency between regions (e.g., the southern U.S. and Mexico) and by accurate location information about distant, but coordinated, subregions (e.g., the southern U.S. and Mediterranean Europe). The data provide support for a framework for making geographical estimates in which people strike a balance between 2 principles: the need to keep their knowledge base coherent, and the inertial tendency to resist changing the knowledge base unless it is necessary to maintain coherence.
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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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".