"Carefully and Cautiously": How Canadian Cultural Memory Workers Review Digital Materials for Private and Sensitive Information
Bibliographic record
Abstract
This study is based on semi-structured interviews with digital preservation practitioners working in Canada. Participants included librarians, archivists, a library director, and technical systems staff. The goal was to understand how participants are reviewing for sensitive personal information in their digital materials, what they are doing with that information, and identify the challenges they face in this work. Qualitative research methods used leaned heavily on feminist methodologies (Stanley & Wise; Taylor) and the field of ethnography (Feldman, Bell & Berger; Emerson, Fretz & Shaw). Findings include a summary of current strategies in place (e.g., risk profiling, using software tools for triage, and consultation with donors and/or community), a list of challenges (e.g., IT or systems infrastructure, barriers to access for software tools, funding restrictions, and scale), and a series of recommendations (e.g., increasing staff resources, developing tools geared towards triage, and improving grant requirements to recognize this work).
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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.037 | 0.022 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".