Bibliographic record
Abstract
The face symbol, developed by Herman Chernoff (1973) , is possibly the seminal multivariate point symbol. Cartographically, the symbol has made an appearance several times, but it has often been criticized because designers tend to ignore two key symbol parameters: feature salience and natural correspondence. Feature salience is the concept of perceptually ordering facial features from those that produce the most noticeable changes to those that produce the least noticeable changes. Natural correspondence refers to designing face symbols so that the overall attitudinal labels of the symbols correspond to the overall physical meaning of the mapped data. It is argued here that feature salience and natural correspondence may be treated as special cases of visual attention in relation to symbol design. From this perspective, these symbols deserve a new look cartographically. This research reports on symbol variations, explores feature salience and natural correspondence, addresses user environments and tasks, and speculates on future experimental designs that may lead to more effective map use of this symbol.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".