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
Research funder mandates, such as those from the U.S. National Science Foundation (2011), the Canadian Tri-Agency (draft, 2018), and the UK Economic and Social Research Council (2018) now often include requirements for data curation, including where possible data sharing in an approved archive. Data curators need to be prepared for the potential that researchers who have not previously shared data will need assistance with cleaning and depositing datasets so that they can meet these requirements and maintain funding. Data de-identification or anonymization is a major ethical concern in cases where survey data is to be shared, and one which data professionals may find themselves ill-equipped to deal with. This article is intended to provide an accessible and practical introduction to the theory and concepts behind data anonymization and risk assessment, will describe a couple of case studies that demonstrate how these methods were carried out on actual datasets requiring anonymization, and discuss some of the difficulties encountered. Much of the literature dealing with statistical risk assessment of anonymized data is abstract and aimed at computer scientists and mathematicians, while material aimed at practitioners often does not consider more recent developments in the theory of data anonymization. We hope that this article will help bridge this gap.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.032 | 0.034 |
| Research integrity | 0.000 | 0.000 |
| 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