Bibliographic record
Abstract
Abstract The empirical study of the psychology of color dates back to the 19th century. Important in this line of research is the study of color preferences—wherein stimuli are characterized in terms of three properties: hue (i.e., wavelength), saturation (i.e., vividness), and brightness (i.e., black-to-white quality). Whereas early thinkers doubted the possibility of a systematic study of color preferences due to idiosyncrasies and individual differences in participants’ choices, a substantial body of empirical evidence has emerged to demonstrate that there are reliable regularities in color preference. Specifically, in terms of single colors, there is a clear maximum around blue and a clear minimum around yellow—a pattern also observed in animals. In terms of saturation, people tend to prefer more saturated to less saturated colors, particularly in context-free settings. In turn, results regarding brightness are more equivocal, although overall there appears to be a preference for lighter colors. Perhaps more interesting are the reasons for the aforementioned preference patterns, for which a number of theoretical explanations have been put forth based on physiology, psychophysics, emotion, and ecological objects—each of which enjoys some level of empirical support. The psychological study of color preferences is well poised for further advancement, with downstream effects in a number of settings ranging from consumer products to artworks and architecture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.196 | 0.074 |
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".