<i>In Sundry Languages</i>
Bibliographic record
Abstract
An imaginative space where culturally and linguistically diverse artists can come together to experiment with various modes of theatrical expression, “In Sundry Languages” does not tell one story in a linear fashion. Instead of recreating stories on stage verbatim, it subverts reality by placing real stories in a realm of the seemingly impossible and conspicuously theatrical. It uses a multiplicity of theatre languages and genres: a soft-shoe routine, elements of tanztheater, improvisation with audiences, dramatic scenes, live videos, monologues, songs, poetry, and multimedia. It plays with the inappropriate and the ‘uncomfortable’ asking the audience to constantly keep biases in check or think twice before laughing (or crying) over the show’s multilingual encounters. An immigrant actor keeps auditioning and fails to get any roles, unless taking roles ridiculing his own culture, language or accent. A refugee tries to establish a connection with locals but fails to penetrate the iconic and often superficial Canadian ‘politeness.’ A meeting of two recent newcomers to Canada results in a major misunderstanding and frustration as both reveal opposing beliefs and convictions. A phone sex conversation performed in the dark explores how the language of the ‘Other’ can become nothing but a fetish. Each multilingual encounter ends in an impasse when the Otherness refuses to be harnessed.]
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 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".