Part of Culture or Toxic Substance? Realities in Transition in Australian and Canadian Alcohol Policy Documents
Bibliographic record
Abstract
This article analyzes alcohol policy documents through the framework of ontological politics developed by science and technology studies theorists John Law and Annemarie Mol. Specifically, it analyzes seven Australian and Canadian documents from 2006 to 2020, focusing on different enactments of alcohol as a harm-producing substance that requires regulation. The article identifies and discusses two co-existing realities of alcohol enacted in these documents: (1) alcohol as part of culture, with benefits and harms manageable through the promotion of moderation; and (2) alcohol as an inherently harmful and toxic substance whatever its pattern of use. The enactment of alcohol as a toxic substance is supported by recent scientific knowledge, in particular the link between drinking and cancer. This second reality of alcohol as toxic is more prominent in the more recent documents; in particular, a transition from one dominant reality to another is clearly apparent in the changes from the 2006–2009 Australian national alcohol strategy to the 2019–2028 strategy. Changes in the dominant reality of alcohol enable or at least support certain policy initiatives while making others less possible and defensible. Focusing on the single reality of alcohol as inherently harmful to health and wellbeing reduces the options for preventing alcohol-related harms.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.038 | 0.037 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| 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".