Comparisons of Residents' Perceptions of Patterns and Trends on Computer-Animated Maps and Graphs and on Google Maps of Events in Their Neighbourhoods
Bibliographic record
Abstract
The present study measures and compares the accuracy of residents' browsing of computer-animated maps and graphs and computer-programmed Google maps in two versions of the Glengarry and Wellington-Crawford Geographical Monitoring Project (GWCGMP). Respondents answered the project's entry questions before browsing displays of the locations and timings of crime and disorder offences, fires, and home sales and prices in their neighbourhoods. Before exiting the project, they answered questions about these events piped from their entry answers and browsed displays. One hundred and seven respondents perceived approximately one-quarter of the “correct” patterns on browsed maps and trends on browsed graphs. These correct patterns and trends were inferred from time-series and linear regression analyses of data on offences, fires, and housing sales for the neighbourhoods. In addition, respondents agreed with almost one-half of comparisons between the patterns and timings of their own events and those displayed on maps and graphs. In conclusion, Web-savvy, younger, spatially active residents are inferred to have been more accurate in perceiving online map patterns and graph trends. They were more accurate than casual stay-at-home browsers, who were more likely not to remember patterns, trends, or displays than they were to perceive them incorrectly.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".