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Record W2028212806 · doi:10.1080/1606635021000021368

Alcohol in Danish and German Educational Print-Media (1990-1998): A Comparison

2003· article· en· W2028212806 on OpenAlexfundno aff
Judith Rosta

Bibliographic record

VenueAddiction Research & Theory · 2003
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersMcMaster University
KeywordsDanishGermanIdeologyAbstinenceConsumption (sociology)Alcohol consumptionAlcoholPrint mediaService (business)Promotion (chess)Political sciencePsychologyAdvertisingBusinessSociologyNewspaperLawHistorySocial scienceMarketing

Abstract

fetched live from OpenAlex

Denmark and Germany have similarities regarding their drinking patterns and alcohol policies, although they differ in their way of health promotion concerning alcohol consumption: the governmental health service in Denmark tending more towards a moderate consumption, and Germany tending rather towards near-total abstinence. The aim of this study is to demonstrate the influence of particular interest groups on alcohol policy by analyses of educational print-media about alcohol. The results show that German educational print-media denounce alcohol as a dangerous drug, while the Danish health service balances the positive effects of alcohol against its negative ones. Alcohol-related problems are related to harmful alcohol consumption in Denmark, and to any alcohol consumption in Germany. The ideological background for these strategies is connected to medical circles in Denmark, and temperance groups in Germany. In Germany, the strong position of the temperance groups supported by the then ruling conservative party, and the national virtuous attitude to temperance, most likely account for the more "restrictive" German approach.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.078
GPT teacher head0.417
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2003
Admission routes1
Has abstractyes

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