Bibliographic record
Abstract
The purpose of this study is to collect a dataset of real online dating profiles to be used and rated by participants in future studies. We began Phase 1 of this study in April 2021 where originally, participants were asked to submit screenshots of their dating profile and rate other profiles. However we were not sure how willing participants would be to provide a screenshot of their real dating profile, so we gave them the option to construct a profile within the survey. Participants were then invited back to complete a follow up survey of their personality measures. To our surprise, many participants were willing to submit screenshots of their profiles, and this served as a more ecologically valid approach than the profiles we artificially constructed based on information submitted by participants if they chose that option. So, in June 2021 we amended the study to include only one survey in which participants filled out background measures and submitted screenshots of their real online dating profiles at the end. The personality measures in Phase 2 were changed slightly from Phase 1. For both phases of the study, participants were required to be 18 years of age or older, living in the U.S.A. or Canada, and currently seeking a relationship. The measures participants filled out for Phase 1 and Phase 2 are available in the attached codebook.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.032 |
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".