Laughing in Pandemic Times — with Charles Demers
Bibliographic record
Abstract
My name is Charlie Demers — I’m a Juno-nominated comedian and I’m a BC Book Prize -nominated author, and my voice features prominently on your parents’ favourite public radio show & your child’s favourite Netflix cartoon (so long as your parents listen to CBC’s The Debaters and your kid watches either of the Emmy-winning programs Beat Bugs or The Last Kids on Earth). I’ve led an idiosyncratic life that has included, among other things, membership in a communist sect; opening, on various occasions, for Sarah Silverman, Marc Maron, Hannibal Buress, Bob Odenkirk & David Cross; organizing a union at a video arcade; making a cooking show pilot with my mother-in-law; writing jokes with Dave Foley of The Kids in the Hall; introducing Noam Chomsky at an anti-war rally of more than fifteen thousand people at Vancouver’s Sunset Beach; sharing a bill talking about anxiety and obsessive-compulsive disorder along with Stanley Cup finalist Kelly Hrudey & his daughter; learning a Cantopop love song for my wedding banquet; and, most recently, going to seminary.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.025 |
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".