Bibliographic record
Abstract
[This is] an earthy, uninhibited, rollicking production… Neptune’s “Dream” is rooted in reality… In fact, the fairies shook me a little… the combined effects of black-light on fluorescent face-paint, luminous costumes fashioned out of leaves and vines and strange guttural sounds made me think of Zulus rather than delicate fairy figures flitting through the forest. Robust is the word that comes to mind, and a robust and very Canadian Puck played in a rather captain-of the-Lacrosse-team way by Margo Sweeny. Neptune’s fairyland seems composed of black velvet and bright artificial flowers that all look like man-eating plants. Rather like a trip on LSD. But creating a strange and otherworldly atmosphere. Kenneth Pogue … comes on strong in both parts [Theseus and Oberon]—lots of authority and stage presence—but not enough contrast in the two roles. To me, none of the fairy kingdom were devilish enough—they were after all, a pretty mean bunch, playing cruel psychological tricks on each other and on the mortals who strayed into their fairyland. Diana Barrington plays the other dual role—Hippolyta… and Titania. The Amazon overshadowed the fairy—but in a regal and charming manner. These two gave us a royal couple, a strong pivot for the play to revolve around. Now we come to the scene-stealers… Denise Fergusson, as Helena. She’s a delightful comedienne—the sad/funny clown—woebegone when Demetrius spurns her—incredulous when he becomes besotted with her… This girl achieves great rapport with her audience… and her timing is near perfect. Then Kenneth Wickes and Doug Chamberlain as Francis Flute and the brash Nick Bottom. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.193 | 0.120 |
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".