Bibliographic record
Abstract
January 01 2018 Imagined Theatres: Writing for a Theoretical Stage Imagined Theatres: Writing for a Theoretical Stage, edited by DanielSack. London and New York: Routledge, 2017. Melanie Bennett Melanie Bennett Melanie Bennett is a performance artist and PhD candidate in theatre and performance studies at York University. She is currently working on her dissertation, entitled A Dramaturgy of Failure in Canadiana. Search for other works by this author on: This Site Google Scholar Author and Article Information Melanie Bennett Melanie Bennett is a performance artist and PhD candidate in theatre and performance studies at York University. She is currently working on her dissertation, entitled A Dramaturgy of Failure in Canadiana. Online Issn: 1537-9477 Print Issn: 1520-281X © 2018 Performing Arts Journal, Inc.2018Performing Arts Journal, Inc. PAJ: A Journal of Performance and Art (2018) 40 (1 (118)): 98–99. https://doi.org/10.1162/PAJJ_r_00399 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 Melanie Bennett; Imagined Theatres: Writing for a Theoretical Stage. PAJ: A Journal of Performance and Art 2018; 40 (1 (118)): 98–99. doi: https://doi.org/10.1162/PAJJ_r_00399 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 JournalsPAJ: A Journal of Performance and Art Search Advanced Search This content is only available as a PDF. © 2018 Performing Arts Journal, Inc.2018Performing Arts Journal, Inc. 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 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; 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".