Balancing Care and Authenticity in Digital Collections
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
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
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 | 0.000 | 0.000 |
| 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 | 0.011 |
| Open science | 0.000 | 0.000 |
| 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