Review: Burn with Desire: Photography and Glamour Anti-Glamour: Portraits of Women
Bibliographic record
Abstract
Book Review| May 01 2015 Review: Burn with Desire: Photography and Glamour Anti-Glamour: Portraits of Women Burn with Desire: Photography and Glamour Anti-Glamour: Portraits of Women RYERSON IMAGE CENTRETORONTO JANUARY 21–APRIL 5, 2015 Jill Glessing Jill Glessing JILL GLESSING teaches and writes on art and culture in Toronto. Search for other works by this author on: This Site PubMed Google Scholar Afterimage (2015) 42 (6): 31–32. https://doi.org/10.1525/aft.2015.42.6.31 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Jill Glessing; Review: Burn with Desire: Photography and Glamour Anti-Glamour: Portraits of Women. Afterimage 1 May 2015; 42 (6): 31–32. doi: https://doi.org/10.1525/aft.2015.42.6.31 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 ContentAfterimage Search This content is only available via PDF. © 2015 Afterimage/Visual Studies Workshop, unless otherwise noted. Reprints require written permission and acknowledgement of previous publication in Afterimage.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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.019 |
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".