E-Commerce and Internet Dating Websites:Differences in Match Options Between African-Americans andWhites
Bibliographic record
Abstract
Internet technology has offered many options for online dating. We studied online dating website capabilities and the relationship between African-Americans and White college students (n=101) regarding their preferences for receiving a weekly list of potential matches via e-mail, receiving free assistance in enhancing the written portion of their profile, the ability to sort matches by ethnicity or religion, and the ability to sort matches based on similar interests. Analysis of variance (ANOVA) showed a significant difference between African-Americans and Whites regarding the ability to receive weekly lists of potential matches via e-mail, where African-Americans had greater means indicative of their preference for this feature (p 0.05). Traditional dating approaches typically involve a man initiating contact with a woman. African-American men may have unique psychological concerns with regard to online dating. They may prefer to wait for automated information about prospective matches rather than initially searching online for prospective matches. An understanding of the gender differences among African-Americans can help understand if it is necessary to advocate a change in this behavioral pattern among African-American men.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".