Bibliographic record
Abstract
This article considers preliminary findings from ethnographic fieldwork undertaken in Australia and Canada in do-it-yourself (DIY) libraries and archives. These spaces are usually run on small or no budgets, often in squatted or donated spaces, with no paid staff. They are motivated by a DIY ethos, and often have a connection to so-called underground communities. In this article the author responds to Chris Attons model of librarian-as-ethnographer, which argues that information workers can draw on ethnographic methods to build cultural maps of grassroots and DIY communities. The author proposes that there are information professionals already in these communities, and their roles in both professional and DIY libraries enhances the librarian-as-ethnographer model by providing an insider perspective that may mediate tensions between the two collection spaces. The author draws on her fieldwork in zine libraries, infoshops, and social centers as example.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".