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Addiction research centres and the nurturing of creativity: The Centre for Addictions Research of British Columbia, Canada

2010· article· en· W1980886396 on OpenAlexafffundabout
Tim Stockwell, Dan Reist, Scott Macdonald, Cecilia Benoit, Mikael Jansson

Bibliographic record

VenueAddiction · 2010
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Victoria
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health Research
KeywordsHarm reductionAddictionPublic relationsPolitical scienceBusinessMedicinePublic administrationSociologyEconomic growthPublic healthNursingPsychiatryEconomics

Abstract

fetched live from OpenAlex

The Centre for Addictions Research of British Columbia (CARBC) was established as a multi-campus and multi-disciplinary research centre administered by the University of Victoria (UVic) in late 2003. Its core funding is provided from interest payments on an endowment of CAD 10.55 million dollars. It is supported by a commitment to seven faculty appointments in various departments at UVic. The Centre has two offices, an administration and research office in Victoria and a knowledge exchange unit in Vancouver. The two offices are collaborating on the implementation of CARBC's first 5-year plan which seeks to build capacity in British Columbia for integrated multi-disciplinary research and knowledge exchange in the areas substance use, addictions and harm reduction. Present challenges include losses to the endowment caused by the 2008/2009 economic crisis and difficulties negotiating faculty positions with the university administration. Despite these hurdles, to date each year has seen increased capacity for the Centre in terms of affiliated scientists, funding and staffing as well as output in terms of published reports, electronic resources and impacts on policy and practice. Areas of special research interest include: drug testing in the work-place, epidemiological monitoring, substance use and injury, pricing and taxation policies, privatization of liquor monopolies, polysubstance use, health determinants of indigenous peoples, street-involved youth and other vulnerable populations at risk of substance use problems. Further information about the Centre and its activities can be found on http://www.carbc.ca.

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)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0180.007
Scholarly communication0.0160.004
Open science0.0030.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0580.009

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 head0.040
GPT teacher head0.327
Teacher spread0.287 · 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.

Study designQualitative
DomainIncentives
GenreEmpirical

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

Citations4
Published2010
Admission routes3
Has abstractyes

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