Bibliographic record
Abstract
The first time I met Marjorie Chan was a couple of years ago at a performance she was starring in at the Solar Stage. She and another actor, Peter van Gestel, were the sole actors in a couple of Tennessee Williams plays, Talk to Me Like the Rain and Let Me Listen and This Property Is Condemned. I was there at her invitation to check out her work for a short film I was trying to make on a shoestring budget and a dream, and finding an Asian actress for the part was not an easy task. It was my first real film and it would be the first time that I had ever worked with actors. I was dealing with a subject – sexual violation – that required some delicacy and sensitivity, both on my part and on the part of the actor. The film was meant to be a visual film poem, without dialogue, and the storytelling needed to be conveyed in slight nuances – very much dependent on the actor and what she brought to it. I had never seen Marjorie perform before, and as the lights dimmed and the play started, I was elated to find that Marjorie could act. I was excited, and when I met her after the performance we agreed that the film would be something we could do together.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.019 |
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".