Gender and Web information seeking: A self‐concept orientation model
Bibliographic record
Abstract
Abstract Adapting the consumer behavior selectivity model to the Web environment, this paper's key contribution is the introduction of a self‐concept orientation model of Web information seeking. This model, which addresses gender, effort, and information content factors, questions the commonly assumed equivalence of sex and gender by specifying the measurement of gender‐related self‐concept traits known as self‐ and other‐orientation . Regression analyses identified associations between self‐orientation, other‐orientation, and self‐reported search frequencies for content with identical subject domain (e.g., medical information, government information) and differing relevance (i.e., important to the individual personally versus important to someone close to him or her). Self‐ and other‐orientation interacted such that when individuals were highly self‐oriented, their frequency of search for both self‐ and other‐relevant information depended on their level of other‐orientation. Specifically, high‐self/high‐other individuals, with a comprehensive processing strategy, searched most often, whereas high‐self/low‐other respondents, with an effort minimization strategy, reported the lowest search frequencies. This interaction pattern was even more pronounced for other‐relevant information seeking. We found no sex differences in search frequency for either self‐relevant or other‐relevant information.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".