Central Questions of Anonymization: A Case Study of Secondary Use of Qualitative Data
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
Anonymization—the removal of identifying information from data—is one way of preparing data for secondary use. This process has not received much attention from scholars, but close examination shows that it is full of methodological, ethical and theoretical tensions. Qualitative research focuses on how people live and act in very particular, situated contexts. Removing identifying information also, inevitably, removes contextual information that has potential value to the researcher. We propose to present a case study of working with anonymized data on the research project, Knowledge Utilization and Policy Implementation, a five-year program funded by the Canadian Institutes of Health Research. This project involves the secondary use of qualitative data sets from multiple separate research projects across Canada. Based on this case study, we provide useful recommendations that address some of the central questions of anonymization and consider the strengths and weaknesses of the anonymization process. URN: urn:nbn:de:0114-fqs0501297
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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