Bibliographic record
Abstract
The at-risk concept is described and its use in the literature on pathological gambling is discussed. An epidemiologic perspective is proposed and the use of risk, at-risk, and not-at-risk are discussed within this framework. It is shown that within the epidemiologic framework the concept of risk applies to nongamblers as well as gamblers, and some nongamblers are theoretically at risk. An example of the application of risk is provided within the context of smoking and the meaning of risk. The frequent assignment of gamblers with scores of 1 or 2 into the same category as those who score 0 is viewed as problematic and is discussed in terms of true negatives and false negatives and the likelihood of pathological gambling among these gamblers. The need for researchers to identify the determinants and indicators of risk is stressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.013 | 0.005 |
| Research integrity | 0.076 | 0.088 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".