Bibliographic record
Abstract
In recent years, university archives have initiated efforts to document mass incarceration in the United States. As they engage in this work, it is important to examine how archivists are responding to the ethical challenges presented by collecting and stewarding records related to incarceration. This article addresses that need by reporting on the findings of qualitative interviews with archivists working at academic repositories with major collections focused on incarceration. This study’s focus on university archives reflects their prominence in undertaking such work, which is likely to continue given these institutions’ comparative autonomy and access to resources. Evaluating this work is urgent because of the vulnerable position of those most impacted by the prison system. Three major themes emerged from the interview data collected in this research: (1) financial and intellectual resources available at universities to support incarceration-related archiving; (2) the university context can provoke ethical anxiety for archivists working with incarceration-related collections; and (3) obtaining meaningful consent is a particularly difficult challenge for archives that steward incarceration materials. Placing these findings within the context of the academy’s carceral entanglements and in dialogue with critical prison studies and critical archival studies scholarship, I argue that ethical incarceration archiving demands a liberatory approach. This approach begins by asking if and how incarceration archiving can help get people free.
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 | 0.004 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".