The Quinte longitudinal study of gambling and problem gambling 2006-2011, Bay of Quinte region, Ontario [Canada]
Bibliographic record
Abstract
The Quinte longitudinal study (QLS) followed a cohort of 4,121 adults living in the Quinte region in southwestern Ontario, Canada for a period of 5 years (2006 to 2011). More specifically, the cohort consisted of people residing within 70 kilometres of the city of Belleville, Ontario. This particular region was chosen as one of the original purposes of the study was to assess the social and economic impact of a new horse race track with slot machines planned for the Quinte region. However, t he planned venue was not built so the study’s exclusive focus became the natural course and etiology of gambling and problem gambling. As part of this investigation, the QLS also investigated the normal patterns of continuity and discontinuity in gambling and problem gambling. The study consists of 1) a Response data set containing all the main QLS variables collected from participant questionnaires and 2) a Process data set that contains variables relevant to the activities and procedures of the research process which were used to assess participant retention rates (e.g. data related to participant-staff interactions and participant activity throughout the 5-year assessment period). An ID variable common to both the Response and Process data sets allows thes e files to be linked, if needed. User manuals and codebooks explaining the data set contents are also provided.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".