Knowledge Organization as Knowledge Creation: Surfacing Community Participation in Archival Arrangement and Description
Bibliographic record
Abstract
Remix or bricolage is recognized as a primary mode of knowledge creation in contemporary digital culture. Archival arrangement represents a form of bricolage that archivists have been practicing for years. By organizing records according to provenance, archivists engage in knowledge creation. Archival theory holds that records are created as an output from social and bureaucratic processes. Archival description, then, could serve as a form of archival record, bearing evidence of the processes of archival arrangement. Current participatory and community-based approaches to archival description urgently require an evidential record of their processes of community consultation and professional mediation. This paper examines two Canadian community-based, participatory archival projects. Project Naming, at Library and Archives Canada, draws upon Inuit community contributions to augment the often sparse and sometimes offensive descriptions of historic photos of arctic peoples. The Sex Work Database at the University of Manitoba, works with sex work activists to create and apply a tagging folksonomy to a collection of websites, organizational records and news media. Analysis of these diverse, community-based projects reveals how current approaches to description make it difficult to distinguish between professional and community contributions to arrangement and description, and proposes ways to make such contributions more apparent.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".