Bibliographic record
Abstract
Lateral II is also, like the first, a braiding of research threads. This time we offer a triple helix. The Cultural Industries thread, curated by Jaafar Aksikas, presents a conversation between two nodes of cultural studies that move in and outside the academy Ien Ang’s Institute for Culture and Society at the University of Western Sydney, and the Cultural Studies Praxis Collective at the University of Washington. This intersectoral work hints at an alter-economy, what it terms a negotiation with partners for critical purchase that complicates the reductive rubrics of neoliberal exchange. The Theory Thread, curated by Patricia Clough features a dossier on digital feminism assembled by Katherine Behar. This too is an effort to find value beyond measure, but also to refuse the algorithms of success, to assert the ungoogleable, the necessary failure, in pursuit of an anti-search engine that might power other reservoirs of thought. The Universities in Question Thread, is curated by student activists Megan Turner and Niall Twohig, and art from the smARTaction collective curated by Tina Orlandini. This dossier of manifestos and art works from various university mobilizations and occupations from Quebec, Cairo, Occupy Wall Street, University of California, and University of Puerto Rico, document the creativity that lies within critical mobilizations and the contagious proliferation of forms that this emergent politics takes.
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.146 | 0.052 |
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".