Bibliographic record
Abstract
This chapter opens with a general discussion on Mike Leigh, who is considered to be Britain's greatest film director and who has carved a unique niche in the film making industry. Among his recent films, Vera Drake was released thirty-four years since his debut feature. Since his return to the cinema, he has consistently written and directed a film every two or three years, with occasional returns to the theatre, the medium in which he began his career. He was not, therefore, an obscure talent who has been waiting to be discovered. Even now more awards have come his way from abroad than at home and much of the critical response to his work in the UK has been ambivalent. The ‘breakthrough’ of Vera Drake was certainly preceded by a turning point in his reputation—and his success at the box office—with the release of Secrets and Lies in 1996, but even that came a quarter of century after Bleak Moments. He worked painstakingly with his actors to create fully rounded characters whose lives and personalities are too complex to be shoehorned into the tidy conventions of realistic drama derived from the theatrical concepts of the well-made play.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.148 | 0.094 |
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".