Social Bases of Psychology Internet Dating: Social Evolution or Revolution
Bibliographic record
Abstract
U.S. and Canada there are over 78.6 million internet users over the age of 16 (Merkle & Richarson, 2000). Forrester Research reports that one in ten people who use the Internet are in a search of relationships, and that nine percent of users in the U.S. use on-line personals (“Americans Look”, n. d.). Additionally, Hardey (2002) reports that “The use of information technology to find and meet a new partner can be traced back to the mid 1960s when an attempt was made to match individuals by comparing data derived from questionnaires using a computer in the United States ” (p. 571). Reports found over 200 sites which offer participants the chance to meet someone (Knox, Daniels, Studivant & Zusman, 2001), and the reasons for meeting can be varied and very specific. Given these facts, it is little surprise that internet dating has become big business, providing those who are willing to pay a membership fee and make the effort to interact in a new way to expand horizons, meet new people, and perhaps settle down. Gilding (2002) points to “a growing consensus that it is the technological revolution that is driving the transformation of society and culture… ” (p. 4). Internet dating is no exception. It is changing the way that people meet and establish relationships.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".