Inconsistency between concept and measurement: The Canadian Problem Gambling Index (CPGI)
Bibliographic record
Abstract
"Problem" and "pathological" gambling represent core concepts that guide gambling research today. However, divergent interpretation of the relation between these terms is continually misguiding the measurement and interpretation of empirical data, and may cumulatively lead to larger-scale problems of conclusion and policy formulation over the next decade. This paper first attempts to unravel the conceptual muddle by outlining the trajectory of the usage of the two terms, from a period where both were dimensionally similar concepts firmly situated in the addiction model to a more recent conception, which takes the view that problem gambling is distinct and properly measured by focusing on the problems that excessive gambling may cause to individuals, families, and communities. We then aim to analyse and criticize the Canadian Problem Gambling Index (CPGI) as a clear example of the confusion of paradigms, an index that defines problem gambling in the newer, problem-centred model, but continues to measure it with items reflecting the older, addiction-centred model. We argue that results obtained using the CPGI, much like those of its predecessors, will not adequately capture the notion of harm that underpins current definitions of problem gambling.
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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 | 0.036 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".