Crowdsourcing Penelope: Margaret Atwood, the Coen Brothers, Richard Linklater
Bibliographic record
Abstract
Beginning with Margaret Atwood's novella The Penelopiad and its staging in regional and experimental theaters in Canada, the U.K., and around the world, this essay traces a long history of network mediation for the Odyssey, beginning with the performative decisions made by itinerant rhapsodes, who variously selected, arranged, and circulated the Homeric epics in ancient Greece, and extending well into the twenty-first century, in the self-conscious invocations of the Odyssey by filmmakers such as the Coen Brothers and Richard Linklater. Crowdsourcing, the unruly and never-ending input from masses of people, is the morphological ground of the epic, an art form energized by its downward percolations. Penelope, that infinitely patient and longsuffering wife, becomes a variety of things as a result, much changed since Homer's times. In the hands of Atwood, she shares the stage with her twelve maids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".