Absolutist, Pragmatist and Realist Approaches to Research Ethics in the Digital Humanities: The Case of the Schneerson Collection
Bibliographic record
Abstract
Ethical absolutism would prohibit any use by researchers of material initially obtained illegally or unethically, whereas ethical pragmatism would determine whether any offsetting benefit could be salvaged from data obtained from irreversible injustices. Ethical realism would determine whether data was originally collected under circumstances that would have been considered illegal, dishonest or unduly coercive at time and, if so, whether its use would create lasting, negative impacts on a particular people or community. Testing three ethical approaches against subjects of digital humanities research, such as WikiLeaks, indicates that ethical realism is most suitable one. Applying ethical realism to case of Schneerson Collection leads to conclusion that digital humanities researchers should be permitted access to part of Collection referred to as the Library, which consists of sacred Jewish texts and books dating back to 1772, but it is advisable for access to part referred to as the Archive, which consists of handwritten notes and correspondence of several generations of Rebbes, to be restricted to persons specifically approved by Chabad organization.
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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 | 0.098 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.110 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.012 |
| 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; 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".