Are Lower-Strength Beers Gateways to Higher-Strength Beers? Time Series Analyses of Household Purchases from 64,280 British Households, 2015–2018
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: Buying and consuming no- (per cent alcohol by volume, ABV = 0.0%) and low- (ABV = >0.0% and ≤ 3.5%) alcohol beers could reduce alcohol consumption but only if they replace buying and drinking higher-strength beers. We assess whether buying new no- and low-alcohol beers increases or decreases British household purchases of same-branded higher strength beers. METHODS: Generalized linear models and interrupted time series analyses, using purchase data of 64,280 British households from Kantar Worldpanel's household shopping panel, 2015-2018. We investigate the extent to which the launch of six new no- and low-alcohol beers affected the likelihood and volume of purchases of same-branded higher-strength beers. RESULTS: Households that had never previously bought a same-branded higher-strength beer but bought a new same-branded no- or low-alcohol beer were less than one-third as likely to go on and newly buy the same-branded higher-strength product. When they did later buy the higher-strength product, they bought half as much volume as households that had not bought a new same-branded no- or low-alcohol beer. For households that had previously purchased a higher-strength beer, the introduction of the new same-branded no- or low-alcohol beer was associated with decreased purchases of the volume of the higher-strength beer by, on average, one-fifth. CONCLUSIONS: The increased availability of new no- and low-alcohol beers does not seem to be a gateway to purchasing same-branded higher-strength beers but rather seems to replace purchases of these higher-strength products. Thus, introduction of new no- and low-alcohol beers could contribute to reducing alcohol consumption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it