A Pinch of Ethics and a Soupçon of Home Cooking: Soft-Selling Supermarkets on Food Television
Bibliographic record
Abstract
On 27 August 2013, Australian commercial broadcaster Network Ten screened a new reality show, Recipe to Riches , in a primetime slot. Based on a Canadian format of the same name, the show sees contestants — ordinary people with no formal training or food credentials — competing for the prize of having their homemade recipes recognised as worthy of being top-selling supermarket products. This chapter discusses the Australian version of this somewhat unusual reality show, situating the rise of the format in the broader contexts of the increasing politicisation and scrutiny of food production and provenance as well as the role of agribusiness and supermarket players in Australia and internationally. Reality-based food shows like MasterChef Australia (Network Ten 2009-) have proved to be highly successful commercial ventures, integrating ‘below-the-line’ advertising and commodities seamlessly into their format structure and content. Sponsored by major Australian supermarket chain, Woolworths, Recipe to Riches takes this commercial logic considerably further. Turning the recipes of ordinary Australians into mass products through a large-scale ‘batch up’ process in a (purportedly) commercial kitchen, the show’s narrative involves developing a branding strategy and a product launch, finally resulting in its temporary placement on Woolworth’s shelves, at which point viewers get to vote for their favourite product by buying it in-store or online. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".