Bibliographic record
Abstract
Drawing on data from semi-structured interviews with five archivists, this article explores the barriers to implementing critical practices in archives. It examines if and how archivists use critical practices and approaches to change archival institutions, highlights the barriers they encounter when trying to transform these institutions, and expands archival scholarship on slowness and neoliberalism. Due to archives’ neoliberal entanglements and their slow pace of change, this research data underscores the powerlessness some archivists feel regarding their ability to change the workings of archival institutions. This article shows that creativity, outlined as experimentation and imagination, can provide a bridge between ideas of critical archival studies and particular contexts of practice and conveys some archivists’ view that transforming practice is unfortunately and necessarily slow. Slowness structures the types of responses and critical practices in archival institutions, setting the parameters for what transformations are available. Slowness provides an opportunity for archivists to reflect on the best way to implement practices but also limits the formation of liberatory praxis in institutional archives.
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 | 0.033 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| 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".