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Record W6969075138 · doi:10.5683/sp3/9oqjoc

Manitoba Longitudinal Study of Young Adults [Canada]

2022· dataset· en· W6969075138 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLongitudinal studyYoung adultSample (material)Longitudinal dataCommissionEpidemiologyAddictionData collection

Abstract

fetched live from OpenAlex

The Manitoba Longitudinal Study of Young Adults (MLSYA) was a five-year longitudinal study conducted by Liquor and Gaming Authority of Manitoba (LGA), formerly the Manitoba Gaming Control Commission (MGCC); the Addictions Foundation of Manitoba; and the former Manitoba Lotteries Corporation. The study followed a sample of Manitobans between the ages of 18 and 20 from 2007 and 2011. The aim of the study was to develop a better understanding of protective facts that promote responsible gambling and risk factors for gambling-related harms. In addition to gambling-specific data, the MLSYA study includes: Psychosocial indicators Demographic characteristics Measures of alcohol and drug use among participants The final sample for all waves of data included 516 participants. Participants were on average 18.9 years of age at recruitment and 22.2 years of age at study completion. LGA commissioned Prairie Research Associates Inc. to recruit participants and collect data for the MLSYA. While the sample is not truly random, it is reasonably representative of the Manitoba population, other than an overrepresentation of participants living in Winnipeg. Participants were recruited through various methods. This included random-digit dialing, onsite casino recruitment, and advertisements at post-secondary institutions and VLT lounges. At each subsequent wave, past participants were contacted and asked to take part in the next wave of the study. Wave 1 data was collected between November 2007 and October 2002. Wave 2 data was collected between December 2008 and December 2009. Wave 3 data was collected between May and December 2010. Wave 4 data was collected between May and December 2011. Additional information on sampling, retention, study variables, and survey questionnaires can be found in the accompanying summary report and codebook.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.017
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0060.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.026
GPT teacher head0.266
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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