Accounting for context: exploring the role of objects and spaces in the consumption of alcohol and other drugs
Bibliographic record
Abstract
This paper addresses some of the major implications of actor-network theory (ANT) for research on the consumption of alcohol and other drugs (AOD). It focuses on the significance of ANT's rejection of the subject–object distinction for recent debates in human and cultural geography regarding the role of social contexts in mediating the character and experience of AOD consumption. In exploring this theme, I apply insights derived from the work of Bruno Latour and John Law to the analysis of qualitative data recently collected in studies of AOD use in Melbourne, Australia and Vancouver, Canada. These studies indicate that AOD consumption is a relational achievement involving diverse objects, spaces, actors and affects. The paper goes on to argue that social contexts may themselves be understood as discrete assemblages of such objects, spaces and actants. This suggests a novel basis for investigating the role of social contexts in mediating AOD consumption in particular sites and settings. The paper closes with an assessment of the implications of these arguments for the ongoing design of novel, place-based approaches to the study of AOD use within human and cultural geography. I emphasise the need for greater recognition of the agentic force of spaces, objects and actants, such that research designs more sensitive to the dynamics of context might be elaborated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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 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".