Open Sharing of Data on Close Relationships and Other Sensitive Social Psychological Topics: Challenges, Tools, and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article reports on an adversarial (but friendly) collaboration examining the issues that lie at the intersection of confidentiality and open-data practices. We describe the process we followed to share our data for a speed-dating article we recently published in Psychological Science (Joel, Eastwick, & Finkel, 2017) and provide a summary of the issues we considered and addressed along the way. As we drafted the present article, the third author became unsure, in retrospect, about some of the procedures we had followed, especially if our approach were to be perceived as a model for open-data decisions in other, more typical cases involving nonindependent data. This article addresses these concerns, but also identifies areas of consensus. All three authors agree that there remains an unmet need for guidelines and other resources to help researchers address the challenges of sharing data that cover sensitive topics, particularly nonindependent data collected from pairs and groups (e.g., romantic couples, work teams, therapy groups). We conclude with a discussion of new tools that could be developed to help scholars who have collected such data to increase the transparency of their research while simultaneously protecting the confidentiality of the participants.
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.
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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it