Bibliographic record
Abstract
Book Review| March 01 2015 Tsai Ming-liang and a Cinema of Slowness by Song Hwee Lim: Hou Hsiao-hsien edited by Richard I. Suchenski BOOK DATA Song Hwee Lim, Tsai Ming-liang and a Cinema of Slowness. Honolulu: University of Hawai‘i Press, 2014. $45.00 cloth. 240 pages; Richard I. Suchenski, ed., Hou Hsio-hsien. Vienna: Austrian Film Museum. $32.50 paper. 256 pages. Lauren Treihaft Lauren Treihaft Lauren Treihaft is completing a master's degree in Media Studies at The New School. She has worked at Cineaste magazine, the Museum of the Moving Image, and the Denver Film Society. Her writing has appeared in Keyframe, Indiewire, and the Toronto-based Exclaim! Search for other works by this author on: This Site PubMed Google Scholar Film Quarterly (2015) 68 (3): 96–98. https://doi.org/10.1525/fq.2015.68.3.96 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Lauren Treihaft; Tsai Ming-liang and a Cinema of Slowness by Song Hwee Lim: Hou Hsiao-hsien edited by Richard I. Suchenski. Film Quarterly 1 March 2015; 68 (3): 96–98. doi: https://doi.org/10.1525/fq.2015.68.3.96 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 ContentFilm Quarterly Search This content is only available via PDF. © 2015 by The Regents of the University of California. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press's Rights and Permissions website, http://www.ucpressjournals.com/reprintinfo.asp.2015 Article PDF first page preview Close Modal You do not currently have access to this content.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".