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
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it