Bibliographic record
Abstract
Through analysis of data from interviews with people who shared their stories with two community archives, Texas After Violence Project (TAVP) and South Asian American Digital Archive (SAADA), this article examines how records creators imagine future use and users. Our findings reveal that people create records with concrete ideas of who might access their record and how they might use it. In keeping with community archives research that troubles the sharp delineation between record creator and user, we find that community archives creators are motivated by the need for representational belonging, radical empathy for their communities, and reciprocal archival imaginaries. Many of the participants in our research also describe their story's potential use as a tool for activism and advocacy. Sharing their stories with these uses in mind, participants in our research engaged in what we call prefigurative record creation, a term we use to describe how participants enacted the future they imagine for their communities by sharing their story in the present. Prefigurative record creation constitutes a political act in opposition to the misrepresentation, erasure, and violence that marginalized communities encounter in society. Recognition of prefigurative records creation as such is crucial to helping community archives understand and meet the expectations of their donors.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.066 | 0.028 |
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".