Our Expectations About Archives: Archival Theory Through a Community Informatics Lens
Bibliographic record
Abstract
Archival institutions and the concerns of Community Informatics occupy a great common territory and yet little familiarity (and even less interaction) exists between the discourses. What do community informatics and archives have to talk about? Why are they ostensibly opposed in theory and practice? How might the roadblocks to a growing potential for joint programming be dematerialized and what is the rationale for attempting it? The archives is presented as a site of negotiation between public memory and contemporary aspiration. The proposition of creating primary records within the archives challenges certain assumptions regarding long-term custodianship by taking up the uncertainties of active community identity-building, by transferring the inherent philosophical debate to the archives proper, and by seeking strategies by which the infrastructure and conceptualization of archives would be equipped to facilitate an emerging model of community informatics.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.077 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".