Bibliographic record
Abstract
Even a cursory examination of some current practices in literature and other arts in Canada reveals that we live in a time of cross-pollination between the arts. It is not an accident that we have reached this moment. The cultural shift has been brought about by the influence of pop culture, technology in the arts and media, and computers. The practices also translate a desire to collaborate, to pool creative energy, to break out of the mold of the solitary suffering artist. Art experienced as a collective creative process has different goals from individual artistic pursuits. Communication and exchange constitute a journey towards becoming whole. I will briefly discuss modes of inspiration that generate interaction, Margaret Atwood’s exemplary history of film and stage adaptation, with a focus on The Penelopiad, some other examples of collaboration between writers and other artists, and multi-disciplinary artists.Key words: Literature, inspiration, adaptation, collaboration, multi-disciplinary artists.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.045 |
| Science and technology studies | 0.048 | 0.031 |
| Scholarly communication | 0.035 | 0.007 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".