Old Dogs, New Tricks: Beyond Simpson Le Mesurier, Crime-Comedy and the Telemovie Series
Bibliographic record
Abstract
Roger Simpson and Roger Le Mesurier are veterans of commercial television drama production (from Division 4 to Stingers). In the post-Skase/Bond era of deficit funding, their innovative telemovie series (Halifax f.p., Dogwoman) and crime-comedy series (Good Guys Bad Guys) have tested the boundaries of Australian television's staple drama format, the crime series. Taking actors (Rebecca Gibney, Marcus Graham, Magda Szubanski) as hooks for the networks, the joint venture company Beyond Simpson Le Mesurier has brought elements of sketch comedy and a high-concept film aesthetic to the crime series format. Drawing on private investment and public money (FFC, FilmVic, CTPF), Beyond Simpson Le Mesurier exemplifies the current convergence between the film and television industries. Paradoxically, the local success of Simpson Le Mesurier's series (particularly with a post-Fordist, 18–39-year-old demographic) highlights a crisis in the drama production industry — a crisis precipitated by a dramatic drop in international sales, forcing a return to licence fee productions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".