Bibliographic record
Abstract
As a collective with four to six members at any one time, A Company of Fools’ mandate is to create innovative and accessible pieces based on the works of William Shakespeare. The company, which currently consists of core members Margo MacDonald, Scott Florence, Elizabeth Logue, Stéfanie Séguin and Al Connors, interprets this mandate two ways: as full-length shows performed with their own spin (most often using elements of clown) and as “Shakespeare in a blender” (a collage piece, a show made up of a collection of themes from Shakespeare or Shakespeare rewritten with improvisational elements involved.) In both instances, the result is generally irreverent – a function of the very physical, presentational and interactive style of the players. Described variously as a Shakespeare comedy troupe, an antidote to boring, conventional Shakespeare, and (by one of the members) as what happens when “the Marx Brothers make coffee for the Monty Python gang at a slumber party where everyone is watching Bugs Bunny” (Florence, Interview), Ottawa’s A Company of Fools has maintained its entertaining, exuberant take on Shakespeare by not taking their source material too seriously. Through their adaptations of Shakespeare, especially the re-workings of scenes in new contexts with unexpected twists, the Fools – as they are commonly called in Ottawa – seem to epitomize a most Canadian attribute: their cultural productions do not disrupt Shakespeare as a dominant cultural figure but playfully adapt his writing and situations for their own comedic ends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".