The Neurocognitive and Evolutionary Bases of Sex Differences in Website Design Preferences
Bibliographic record
Abstract
Marketing managers habitually use sex as a form of segmentation since it satisfies several requirements for efficient implementation including profitability, identifiability, accessibility, and measurability (Darley & Smith, 1995). Nevertheless, sex differences in marketing remain under-researched and continue to be a source of confusion for managers (Hupfer, 2002). Sex differences in cognitive processing are particularly relevant to e-business managers given that online consumers must process various types of spatial and perceptual information while navigating online. Despite the large body of evidence documenting consistent sex differences in cognition (Kimura, 2004), there is a paucity of research exploring how male and female consumers respond differently to various website design aspects (Cyr & Bonanni, 2005; Moss, Gunn, & Heller, 2006; Simon, 2001). Moreover, the few studies that have examined sex differences in online preferences were not grounded in any consilient theoretical framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".