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
sponsorship: We wish to thank several people here who have helped in various ways to make the production of this special issue possible. Thanks first to the contributors to the issue, especially to Georges Didi-Huberman. Thanks are due to Mieke Bleyen for her help in liaising with some of the contributors of this issue and in editing and revising. Thanks to Emmanuel Alloa for his help in getting the permission to translate the Didi-Huberman and Ranciere exchange. Additionally, we wish to thank the Research Unit Literary Studies of KU Leuven, led by Bart Philipsen, for providing us with the funds necessary to translate some of these contributions into English. Thanks are due to the FWO (Research Foundation Flanders) and the SSHRC (Social Sciences and Humanities Research Council of Canada) which, by providing support to the co-editors, helped to make this project possible. Thanks to all those who helped in the translation process: Christopher Woodall for his translation of three of the articles in this issue; thanks to Michiel Rys and Jan Vanvelk for their translation of Sigrid Weigel's text and help with other questions; thanks to Elise Woodard and Jorge Rodriguez Solorzano for their translation of the exchange between Didi-Huberman and Ranciere. Thanks to Clarissa Colangelo for help with some Italian sentences and thanks also to Anneleen Masschelein, Stephane Symons and Joost de Bloois for their comments. (FWO (Research Foundation Flanders), SSHRC (Social Sciences and Humanities Research Council of Canada))
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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