Drinking patterns and problems: a search for meaningful interdisciplinary studies.
Bibliographic record
Abstract
This paper outlines an interdisciplinary framework for conducting research on drinking patterns and problems, reflecting a social-ecological perspective on person-environment interactions (Stokols, 1996). The sheer volume of existing alcohol research precludes a systematic and thorough review of all relevant sources. This paper presents a set of arguments about implicit disciplinary and methodological assumptions that have slowed the development of meaningful interdisciplinary approaches to research on drinking patterns and problems. The longer online version of the paper presents these arguments in detail and outlines basic elements of a conceptual framework for research that involves three central constructs studied at four levels of analysis and incorporating three distinct methodological perspectives. That version presents selected empirical studies and theoretical statements with reference to the coordinates provided by these dimensions. Problems in Formulating an Interdisciplinary Conceptual Framework Implicit Assumptions About Appropriate Levels of Analysis One prominent approach to the study of drinking patterns and problems emphasizes the occurrence, distribution, and determinants of alcohol use and its consequences in populations. This tradition imports collectivist assumptions from epidemiology and sociology by using national-, regional-, and community-level measures of drinking patterns and problems. Several traditions within this approach can be identified, each adopting its own measurement strategies for assessing drinking patterns and problems (Babor, 1990). For example, Grant (1993) distinguishes among three epidemiological perspectives on population-level drinking phenomena. From the perspective of psychiatric epidemiology, discrete or categorical measurement strategies are used to classify populations with
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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 | 0.080 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".