LMDA Conference Sessions Manual, 2018
Bibliographic record
Abstract
iTunes charts and the rest, as they say, is herstory.Named the Best Podcast of 2017 by CBC Arts and The Globe and Mail. 11:20 -11:30am --Plenary Session --LBTC Session Room 1Introduction to the #lmdaMeToo Story Collection Moderator: Ilana Brownstein As #metoo has taken shape over the last year, many have spoken their truths for the first time, awakening to the scope of the issue in communities large and small.The dramaturgy field poses a particular set of concerns, and many are silenced around issues of sexual harassment and hostile work environment.• #lmdaMeToo is a vehicle by which those who wish to make their experiences visible can do so, anonymously or not.#lmdaMeToo is for people of any gender expression, sexual identity, age or professional expertise.#lmdaMeToo is not an investigative organ.It's a temperature gauge.Thursday, June 21 11:30 -1:00pm --Regional Lunches Join your LMDA Regional Reps and get to know other dramaturgs and literary managers in your region.For those who reserved their lunch while registering, you can pick yours up in the conference lobby.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.883 | 0.796 |
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".