Bibliographic record
Abstract
What happens when we take the joke of “lesbian processing” seriously as a research method? Heavy Processing does just this, by tracing the multi-directional genealogies and vast affinities of processing-heavy methods as innovations in information technologies (such as operating systems, central processing units, network designs). Part methods handbook, manifesto, and survival guide, this book opens up the fields of information studies, data studies, digital media studies, and digital humanities to critical digital methods, information technologies, and infrastructures: trans- feminist and queer (TFQ) cultural protocols and ways of working. Cowan and Rault offer heavy processing as a maximalist research method, consistent with a long and proud lesbian-leaning TFQ tradition of making a mountain out of a molehill. Heavy Processing draws together activist, artistic, and scholarly work that is both about and not about digital materials to critically reorient digital research methods calibrated for accountability, relationship-building, and trust as measures of scholarly rigor. A raging romp of a methods manual, Cowan and Rault offer an alternative to mass digitization in the form of TFQ processing for analog and born digital materials. They write for students, faculty, and researchers, as well as for information, cultural heritage, and tech-sector professionals; for anyone interested in digital media and feminist, queer, and transcultural studies; and for anyone who has ever been studied.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.276 | 0.155 |
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".