Bibliographic record
Abstract
Many community-led archives have long challenged traditional archival description by prioritizing relational, political, and affective approaches to metadata. While institutional metadata standards emphasize consistency and standardization, they often fail to account for the needs of marginalized communities, reinforcing dominant narratives while excluding alternative forms of knowledge. This paper examines how engaging with archival description as a living, evolving process can instead turn metadata into a tool of radical care. Through case studies of the Sexual Minorities Archives, Transas City, and the Plateau Peoples’ Web Portal, this paper explores how participatory and empathy-driven metadata practices resist normative archival frameworks and instead foster networks of care, accessibility, and belonging. These case studies demonstrate that metadata is not merely a technical tool but a political and ethical instrument that can empower historically excluded communities. Ultimately, this paper argues that sustainable metadata practices must centre harm reduction, relational care, and community sovereignty to ensure that archives remain accessible, meaningful, and representative for present and future community users.
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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.099 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".