Advertising slogans in the gambling industry: content analysis informed by the heuristics and biases literature
Bibliographic record
Abstract
In this paper, we analyse the contents of over a thousand gambling slogans. We identify several features considered in the literature that the slogans might capitalize on. In particular, we investigate heuristics and biases analyzed in the behavioural economics of decision making under risk, such as the gambler’s fallacy. We then employ factor analysis to identify the main types of heuristics and biases showing up in the analyzed slogans. We find three naturally interpretable factors and show that they intuitively correlate with the type of game each slogan advertised. We also construct an index of potentially dangerous features a slogan might have and show that their use subsided slightly in the UK after the Industry Code for Socially Responsible Advertising was implemented in 2007.RésuméCet article porte sur l’analyse de plus d’un millier de slogans sur les jeux de hasard. Nous retrouvons dans ces slogans de nombreuses caractéristiques évoquées dans la littérature sur le sujet, en particulier les heuristiques et biais analysés en économie comportementale dans la prise de décision en situation de risque, comme l’illusion du joueur. Au moyen d’une analyse factorielle, nous dégageons les trois principaux types d’heuristiques et de biais qui se manifestent dans les slogans étudiés. Nous décelons ensuite trois facteurs interprétables et démontrons leurs corrélations intuitives avec le type de jeu dont chaque slogan fait la promotion. Enfin, nous proposons un index des caractéristiques potentiellement dangereuses des slogans sur les jeux de hasard et démontrons que l’emploi de ceux-ci a diminué légèrement au Royaume-Uni après l’adoption du Gambling Industry Code for Socially Responsible Advertising en 2007.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".