Reactions of industry and associated organisations to the announcement of the UK Soft Drinks Industry Levy: longitudinal thematic analysis of UK media articles, 2016-18
Bibliographic record
Abstract
Abstract Background The UK Soft Drinks Industry Levy (SDIL) was announced in March 2016, became law in April 2017, and was implemented in April 2018. Empirical analyses of commercial responses have not been undertaken to establish the scale, direction or nuance of industry media messaging around fiscal policies. We aimed to develop a detailed understanding of industry reactions to the SDIL in publicly available media, including whether and how these changed from announcement to implementation. Methods We searched Factiva to identify articles related to sugar, soft-drinks, and the SDIL, between 16th March 2016–5th April 2018. Articles included were UK publications written in English and reporting a quotation from an industry actor in response to the SDIL. We used a longitudinal thematic analysis of public statements by the soft-drinks industry that covered their reactions in relation to key policy milestones. Results Two hundred and ninety-eight articles were included. After the announcement in March 2016, there was strong opposition to the SDIL. After the public consultation, evolving opposition narratives were seen. After the SDIL became law, reactions reflected a shift to adapting to the SDIL. Following the publication of the final regulations, statements sought to emphasise industry opportunities and ensure the perceived profitability of the soft drinks sector. The most significant change in message (from opposition to adapting to the SDIL) occurred when the SDIL was implemented (6th April 2018). Conclusion Reactions to the SDIL changed over time. Industry modified its media responses from a position of strong opposition to one that appeared to focus on adaptation and maximising perceived profitability after the SDIL became law. This shift suggests that the forces that shape industry media responses to fiscal policies do not remain constant but evolve in response to policy characteristics and the stage of the policy process to maximise beneficial framing.
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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 | 0.019 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".