The development of online cross-group relationships among university students
Bibliographic record
Abstract
With our social lives increasingly experienced online, it is critical to understand online relationship development. The current study examined the outcomes of disclosing stigmatized group membership (i.e., being gay or lesbian) earlier (vs. later) in a developing online relationship. Heterosexuals ( n = 214) engaged in an experimentally controlled closeness-inducing online interaction with an ostensible partner, learning that he/she was gay/lesbian either before (i.e., earlier) or after (i.e., later) the interaction. Earlier (vs. later) discovery led to a subjectively more positive contact experience, which predicted heightened bond with the partner, itself predicting more positive attitudes toward the partner. Outcomes were uninfluenced by pretest biases and authoritarianism, suggesting the general benefits of disclosing gay/lesbian identity earlier (vs. later) in online relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".