Bibliographic record
Abstract
June 01 2018 Words on Screen Words on Screen by MichelChion; translated by ClaudiaGorbman. Columbia University Press, New York, NY, 2017. 272 pp., Paper. ISBN: 978-0231174992. Will Luers Will Luers The Creative 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 The Creative Media Digital Culture Program, Washington State University Vancouver. Email: wluers@gmail.com. Online Issn: 1530-9282 Print Issn: 0024-094X ©2018 ISAST2018ISAST Leonardo (2018) 51 (03): 318–319. https://doi.org/10.1162/leon_r_01624 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Will Luers; Words on Screen. Leonardo 2018; 51 (03): 318–319. doi: https://doi.org/10.1162/leon_r_01624 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsLeonardo Search Advanced Search This content is only available as a PDF. ©2018 ISAST2018ISAST 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 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 | 0.001 | 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) | 0.015 | 0.011 |
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; both teacher heads agree on what is shown here.
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".