Uncovering electronic cigarette shops 'retail kinship' strategies
Bibliographic record
Abstract
The aim of this paper is to explore how vape shops use retail kinship strategies to create a community of loyal customers. Retailing a product shrouded in rhetoric and controversial benefits is risky: a product that may wean consumers from a nasty habit proven to shorten one’s life span yet is banned in Canada and restricted in Australia with a plethora of laws regulating its use. In the UK and Europe the reality is a welcoming of e-cigs as an effective tool to discourage smoking tobacco. Such a product is not only perilous and controversial but requires customised retail strategies to flourish in a kinship manner devoted to their community of customers. The UK e-cigarette industry is facing paradoxical public opinion, contentious medical reports and ever-changing governmental legislation– welcome to the UK e-cigarette market, an industry worth £913 million and 2.6 million users of vape products. (Goldsmith, 2016).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".