Impacts of Post-Truth Conditions on a Susceptible Market: The Case of Nicotine Vaping
Bibliographic record
Abstract
The post-truth era is characterized by widespread mistrust, competing truth claims, and polarization that impact some markets to a greater degree than others. This study introduces the concept of post-truth markets as those which are highly susceptible to being impacted by post-truth conditions. Three research questions guide this study: Why are some markets susceptible to the impacts of post-truth conditions? What is the impact of post-truth conditions on susceptible markets? And, how do consumers navigate post-truth markets? Taking the nicotine vaping market as an exemplar of post-truth markets, this study uses critical discourse analysis to examine qualitative data, including archival data (legal, news media, industry, and advocacy texts), in-depth interviews with consumers and advocates, and observational data. The theoretical insights generated indicate that markets affected by historical stigma, restrictive authority interventions, and changing expert opinions are susceptible to becoming post-truth markets. Further, the data analysis suggests that post-truth conditions lead to contestation in such markets, including moral contestation which has been noted in prior literature, and epistemic contestation which this study introduces. Consumers develop various strategies based on a post-truth subjectivity to navigate post-truth markets, including alternate truth-seeking (through relational and embodied knowledge), entrepreneurship, and activism. This research introduces several new concepts to consumer research, including the concepts of post-truth markets, post-truth subjectivity, and epistemic contestation. The findings also contribute to the growing literatures on marketplace contestation, activism, stigma, and the role of emotions in consumption. Finally, the findings have implications for various stakeholders in the nicotine vaping market, as well as other post-truth markets.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".