The Impact of Bivariate Symbol Design on Task Performance in a Map Setting
Bibliographic record
Abstract
Research conducted on the theory of selective attention suggests that varying the graphic combinations used when designing bivariate symbols affects the functionality of the symbol. Some graphic combinations appear to facilitate the ability to visualize correlation between the data sets represented by the symbol; others appear to be more effective at representing the data sets individually, some even at the expense of extracting correlational information. The purpose of the research described here was to test the strength of these findings in a map use context. Several bivariate symbol designs were tested using map use tasks designed to test participants' abilities to extract either correlational or individual information. Participant reaction times provided an assessment of the types and levels of interactions that occurred with each symbol set. Results corroborate previous research in both cartography and psychology, with several symbol designs falling into each of three interactional categories: separable, integral, and configural. By confirming and expanding previous research, this study provides further evidence of the strength of selective attention theory in aiding the design of bivariate thematic maps.
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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 | 0.006 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".