DIY academic archiving: mischievous disruptions of a new counter-movement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it