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Record W4283805570 · doi:10.1111/dar.13509

Developing harm reduction policies: Evidence from Copenhagen's drug consumption rooms

2022· article· en· W4283805570 on OpenAlexaboutno aff
Jordan M. Hyatt, Synøve N. Andersen, Emily Greberman, Lars Højsgaard Andersen, Ivan Lind Christensen

Bibliographic record

VenueDrug and Alcohol Review · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
FundersNorges Forskningsråd
KeywordsQuarter (Canadian coin)Real estateConsumption (sociology)Rest (music)EconomicsAsset (computer security)BusinessFinanceActuarial scienceMedicineComputer scienceHistoryComputer securityArt

Abstract

fetched live from OpenAlex

Ivan Christensen is the director of Mændenes Hjem. The remaining authors have no conflicts or potential conflicts to declare. Figure S1 Trends in average real estate price in Vesterbro and the rest of Copenhagen, 2010–2014. Figure shows the trends in average real estate prices (price per square metre) in Vesterbro, where Mændenes Hjem opened its permanent drug consumption room in the second quarter of 2012 (marked by the vertical dashed line), and the rest of the city of Copenhagen. Prices are indexed so prices in Vesterbro relative to the rest of Copenhagen are set to 100 in first quarter of 2012, just prior to the opening of the drug consumption room. Results are from authors' calculations using publicly available data from Finance Denmark (a business association for banks, mortgage institutions, asset management, securities trading, and investments funds in Denmark), which consist of the average actual sales prices per square metre of all traded real estate per quarter. Data retrieved on 2 December 2021, from https://rkr.statistikbank.dk/statbank5a/default.asp?w=3440 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.149
GPT teacher head0.410
Teacher spread0.261 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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

Citations3
Published2022
Admission routes1
Has abstractyes

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