Bibliographic record
Abstract
This article explores the challenges of engaging historically excluded communities with archives and archival discourse, focusing on people and communities experiencing homelessness. Positioning the phrase literal homelessness, which is used in the United States to determine eligibility for an annual census of people experiencing homelessness, as representative of ongoing exclusive and non-collaborative forms of recordkeeping, the author proposes a concept that she calls archival readiness to move toward archive making, rather than archive taking, with historically excluded communities. Using her experiences as a part-time staff member in a temporary emergency shelter that was established during the COVID-19 pandemic, she shows how archival readiness, based on ongoing relationships among archivists, researchers, community organizations, and individuals, would increase the likelihood that shelter guests would participate in archiving. Exploring how homelessness creates challenges for the development of inclusive institutional and community-archiving praxes, she argues that while archival readiness would not solve each of these challenges, it could enable historically excluded communities to participate in generating other approaches. The author enacts archival readiness by sharing three records from the shelter and her interpretations of them, introducing forms of information about shelter living that is not collected in official data that tracks “literal homelessness.”
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.016 |
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".