Bibliographic record
Abstract
In this interview, award-winning musical comedian Alex MacKeith and I examine his screenplay for Alice Troughton’s new film The Lesson (2023). MacKeith goes over his creative process, from his incorporation of some of his experiences as a tutor following university to his development of individual characters, and from his project’s growth and improvement across successive drafts to the kinds of affirmation that he gets from his work in musical comedy in comparison to his work in scriptwriting. We go on to discuss his magnificent central character: the writer J. M. Sinclair (Richard E. Grant). The film follows Liam (Daryl McCormack), a young writer hired to prepare Sinclair’s son (Stephen McMillan) for Oxford, but as it unfolds, we learn, alongside Liam, about Sinclair’s personal and public lives. Sinclair’s wife Hélène (Julie Delpy) hired Liam specifically to investigate him. A living cliché, Sinclair is claiming credit for work that really belongs to his deceased son Felix (Joseph Meurer). For this film, MacKeith was longlisted for a British Independent Film Award. It has been described by Stephen King as being ‘[c]old, smart, and suspenseful’ and it is a New York Times’s Critics Pick. In this climate, when conversations about originality, notably the place of artificial intelligence in creative and critical projects, are all the rage, this interview’s exploration of authorship, intellectual property and retirement for creatives is especially timely.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".