Open Sharing of Data on Close Relationships and Other Sensitive Social Psychological Topics: Challenges, Tools, and Future Directions
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.
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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 | 0.665 | 0.640 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.025 | 0.098 |
| Scholarly communication | 0.053 | 0.122 |
| Open science | 0.017 | 0.068 |
| Research integrity | 0.019 | 0.037 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".