The Sweetened Beverage Tax in Cook County, Illinois: Lessons From a Failed Effort
Bibliographic record
Abstract
Objectives. To describe the public health and policy lessons learned from the failure of the Cook County, Illinois, Sweetened Beverage Tax (SBT). Methods. This retrospective, mixed-methods, qualitative study involved key informant (KI) and discussion group interviews and document analysis including news media, court documents, testimony, letters, and press releases. Two coders used Atlas.ti v.8A to analyze 321 documents (from September 2016 through December 2017) and 6 KI and discussion group transcripts (from December 2017 through August 2018). Results. Key lessons were (1) the SBT process needed to be treated as a political campaign, (2) there was inconsistent messaging regarding the tax purpose (i.e., revenue vs public health), (3) it was important to understand the local context and constraints, (4) there was implementation confusion, and (5) the media influenced an antitax backlash. Conclusions. The experience with the implementation and repeal of the Cook County SBT provides important lessons for future beverage tax efforts. Public Health Implications. Beverage taxation efforts need to be treated as political campaigns requiring strong coalitions, clear messaging, substantial resources, and work within the local context.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".