The role of categories and spatial cuing in global-scale location estimates.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Seven independent groups estimated the location of North American cities using both spatial and numeric response modes and a variety of perceptual and memory supports. These supports included having location markers for each city color coded by nation and identified by name, giving participants the opportunity to see and update all their estimates throughout the task, and allowing them to respond directly on a map. No manipulation mitigated the influence of categories on the judgments, but some manipulations improved within-region ordinal accuracy. The data provide evidence that the city and regional levels are independent, spatial and numeric response modalities affect accuracy differently at the different levels, biases at the regional level have multiple sources, and accurate spatial cues improve estimates primarily by limiting the use of global landmarks to partition the response space. Results support J. Huttenlocher, L. V. Hedges, and S. Duncan's (1991) theory of spatial location estimates and extend it to the domain of real-world geography.
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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 | 0.000 |
| 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 it