<i>Toward Fewer Images: The Work of Alexander Kluge, 1st Edition</i> by Philipp Ekardt. MIT Press, Cambridge, MA, U.S.A., 2018. 448 pp., illus. Trade. ISBN: 978-1517903206.
Bibliographic record
Abstract
October 01 2019 Toward Fewer Images: The Work of Alexander Kluge, 1st Edition Toward Fewer Images: The Work of Alexander Kluge, 1st Edition by PhilippEkardt. MIT Press, Cambridge, MA, U.S.A., 2018. 448 pp., illus. Trade. ISBN: 978-1517903206. Will Luers Will Luers Media Digital Culture Program, Washington State University Vancouver. Email: wluers@gmail.com. Search for other works by this author on: This Site Google Scholar Author and Article Information Will Luers Media Digital Culture Program, Washington State University Vancouver. Email: wluers@gmail.com. Online Issn: 1530-9282 Print Issn: 0024-094X ©2019 ISAST2019ISAST Leonardo (2019) 52 (5): 509–510. https://doi.org/10.1162/leon_r_01814 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Will Luers; Toward Fewer Images: The Work of Alexander Kluge, 1st Edition. Leonardo 2019; 52 (5): 509–510. doi: https://doi.org/10.1162/leon_r_01814 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll JournalsLeonardo Search Advanced Search This content is only available as a PDF. ©2019 ISAST2019ISAST Article PDF first page preview Close Modal You do not currently have access to this content.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.243 | 0.267 |
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".