Parliamentary reaction to the announcement and implementation of the UK Soft Drinks Industry Levy: applied thematic analysis of 2016–2020 parliamentary debates
Bibliographic record
Abstract
Abstract Objective: The UK Soft Drinks Industry Levy (SDIL) (announced in March 2016; implemented in April 2018) aims to incentivise reformulation of soft drinks to reduce added sugar levels. The SDIL has been applauded as a policy success, and it has survived calls from parliamentarians for it to be repealed. We aimed to explore parliamentary reaction to the SDIL following its announcement until two years post-implementation in order to understand how health policy can become established and resilient to opposition. Design: Searches of Hansard for parliamentary debate transcripts that discussed the SDIL retrieved 186 transcripts, with 160 included after screening. Five stages of Applied Thematic Analysis were conducted: familiarisation and creation of initial codebooks; independent second coding; codebook finalisation through team consensus; final coding of the dataset to the complete codebook; and theme finalisation through team consensus. Setting: The United Kingdom Parliament Participants: N/A Results: Between the announcement (16/03/2016) – royal assent (26/04/2017), two themes were identified1: SDIL welcomed cross-party 2: SDIL a good start but not enough.Between royal assent – implementation (5/04/2018), one theme was identified3: The SDIL worked – what next?The final theme identified from implementation until 16/03/2020 was4: Moving on from the SDIL. Conclusions: After the announcement, the SDIL had cross-party support and was recognised to have encouraged reformulation prior to implementation. Lessons for governments indicate that the combination of cross-party support and a policy’s documented success in achieving its aim can help cement the resilience of it to opposition and threats of repeal.
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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 | 0.047 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".