Data Deidentification for Data Sharing in Education and Psychological Research: Importance, Barriers, and Techniques
Bibliographic record
Abstract
In this manuscript, we discuss the importance of data sharing in educational and psychological research, emphasizing the historical context of data sharing, the current open science movement, and the so-called replication crisis. We additionally explore the barriers to data sharing, particularly the fear of incorrectly deidentifying data or accidentally including private information. We then highlight the importance of deidentifying data for data sharing. Finally, we present specific techniques for data deidentification, namely non-perturbative and perturbative methods, and make recommendations for which techniques are relevant for specific types of variables. To assist readers in implementing the material from this study, we have additionally created an interactive tutorial as a Shiny web application, which is publicly available and free to use.
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 | 0.614 | 0.825 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.009 | 0.031 |
| Research integrity | 0.006 | 0.014 |
| 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".