Bibliographic record
Abstract
Both traditional recordkeeping and radical empathy frameworks ask us to carefully consider: the presence of sensitive information within digital content; those who created, are captured by, and are affected by a record (or the absence of that record); and the consequences of retaining or discarding that information. However, automated digital archiving workflows – in order to handle the scale and volume of digital content – discourage contextual and empathetic decision-making in favour of preselected decisions. This paper explores the implications on labor and privacy of the common practice to “take and keep it all” within the context of radical empathy. Practices which promote retention of complete disk images and encourage the creation of access copies with redacted sensitive data are vulnerable. The decision to discard must be deliberate and, often, must be enacted manually, outside of the workflow. The motivation for this model is that the researcher, archivist, curator, or librarian can always return to the original disk image in order to demonstrate authenticity, allow for emulation or access, or to generate new access copies. However, this practice poses ethical privacy concerns and does not demonstrate care. We recognize that the resources necessary to review disk images and make contextual decisions that balance both privacy and authenticity are sizable due to the manual nature of this work: this places strain and further labor on staff and practitioners using current digital archival and preservation tools. We proffer that there is a need to develop tools which aid in efficient and explicit redaction, but also allow for needed contextual and empathetic decision-making. Further we propose that more staff time is required to make these decisions and if that staff time is not available, then the institution should consider itself incapable of ethically stewarding the content and protecting those affected. Pre-print first published online 01/24/2021
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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 | 0.095 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.031 | 0.108 |
| Scholarly communication | 0.044 | 0.042 |
| Open science | 0.007 | 0.067 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".