Accounting as a Normalizing Tool for Transitional Dirtiness: The Case of the<scp>US Adult‐Use</scp>Cannabis Industry*
Bibliographic record
Abstract
ABSTRACT While prior accounting research documents normalizing strategies within the accounting profession and in instances of accounting adoption, the potential of accounting itself as a strategic tool toward normalizing that which is considered socially abnormal (i.e., dirty) remains an important and unexamined area of inquiry. In this study, we conduct in‐depth interviews to examine the role accounting plays in the development of the US cannabis industry (CI) as it transitions from the illicit market in which formal accounting was systematically avoided to a state‐legal market in which participants are subject to conventional business processes. Facing impediments to traditional operating practices and pressures to increase normative conformity for industry survival, cannabis operators (COs) incorporated the use of accounting in three normalizing strategies (creative concession, collaborative facilitation, and experimentation), seemingly influenced by the incongruencies between prior illicit‐market culture and experiences and the state‐legal operating environment. In response to what operators perceived to be coercive regulation, they employed creative concession strategies, including influencing, bargaining, challenging, escaping, and cessation tactics. However, in response to pressures to adopt more commonly accepted forms of accounting, COs instead deployed two different strategies, one focused on acquiescence to normalizing pressures when doing so facilitated essential relationship building (i.e., collaborative facilitation strategies), and one deployed as strategic experimentation, working to normalize industry activities in areas of perceived threats to industry acceptance and continuity. Given CO accounting naiveté, its usefulness was often introduced by peripheral industry parties attempting to normalize their own participation with the CI. Notably, we also find that normalizing pressures occasionally resulted in unintended consequences, including reversion to the illicit market and forgoing normalizing strategies in favor of retaining some level of dirtiness to fend off pending competition, both of which threaten to reemphasize the industry's dirtiness. Our study, therefore, points to accounting itself as a central mechanism in the complex, multidirectional strategy to normalize transitional dirtiness.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".