Central Questions of Anonymization: A Case Study of Secondary Use of Qualitative Data
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
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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 | 0.251 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.031 | 0.038 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".