Uncovering the Nature of Information Processing of Men and Women Online: The Comparison of Two Models Using the Think-Aloud Method
Bibliographic record
Abstract
This paper compares two models predicting gender differences in information processing to determine if either of the models is more pertinent to goal-oriented Internet searches. The Selectivity Model (Meyers-Levy 1989) proposes that women make more comprehension effort than men whereas the Item-Specific/Relational Processing Model (Putrevu 2001) suggests that men and women differ primarily in their processing style, with men tending to use item-specific processing by focusing on product attributes and women tending to use relational processing by looking for interrelationships among multiple pieces of information. The study participants (106 total, 50% female) were asked to think aloud while performing one of two goal-oriented search tasks on a website. Their thoughts were then coded according to relevant categories by two independent analysts using Atlas TI software. Consistent with the Selectivity Model, women made more comprehension effort than did men. However, our hypotheses related to a difference in processing style between men and women received less support. Overall, the results help disentangle the two theories and provide website developers with a basis for creating sites that are suited to men’s and women’s distinctive information processing strategies.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".