But what do participants want? Comment on the “Data Sharing in Psychology” special section (2018).
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
. In 4 articles, the authors outline how open data can positively impact psychology and provide guidelines for adopting open data practices, which we believe is to be commended. However, this special issue has not acknowledged a crucial concern in the open data debate: the views and desires of participants. Participants are the backbone of psychological research and an important stakeholder in open data issues. We review research that has studied participants' opinions of open data and outline concerns regarding open data raised by some groups of participants. We conclude with recommendations, including a call to psychological researchers to move beyond opinion and instead to empirically examine the impact of open data. We believe psychology is a discipline uniquely poised to execute these recommendations and guide researchers' understandings of how to appropriately and ethically implement open data practices across multiple disciplines. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.018 | 0.005 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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