DIY academic archiving: mischievous disruptions of a new counter-movement
Bibliographic record
Abstract
Against increasing injunctions in research governance to create open data, and knee-jerk rejections from qualitative researchers in response to such efforts, we explore a radical counter movement of academics engaged in what we term “DIY Academic Archiving,” the creation of open and accessible archives of their research materials. We turn to interviews with three DIY academic archivists, each drawing on an ethos of community archiving, as opposed to emerging open data schemes: Melissa Munn on The Gaucher/Munn Penal Press Collection , 1 Eric Gonzaba’s Wearing Gay History , 2 and Michael Goodman’s Victorian Illustrated Shakespeare Archive . 3 We see these archives as engaged in a “politics of refusal,” which challenges both conventional methods and ethics in qualitative research as well as new moves toward open data. On the one hand, academics are tasked to “protect” their data by destroying it, under the guise of a supposed mode of “care.” On the other hand, open data makes quite contrary demands, to care for data by making it “open” for further extraction through (re)use. DIY Academic Archiving is a practice of refusal that supports a redirection away from this binary. In this article, we explore how DIY academic archivists play with coding as a form of mischievous disruption, and so are contributing to new data imaginaries. We offer insight into how DIY Academic Archiving supports researchers in their theoretical, methodological and political commitments, and at the same time, how it can enable researchers to take the care-full risk of archiving our research data.
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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 | 0.146 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.055 | 0.176 |
| Scholarly communication | 0.043 | 0.038 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".