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Record W2979743168

Our Common Principles: Exploring Harm Reduction Based Drug Policy as an Avenue for Sustainable Development in Urban Contexts

2019· dissertation· en· W2979743168 on OpenAlexaboutno aff
Alexander Mercado

Bibliographic record

VenueUWSpace (University of Waterloo) · 2019
Typedissertation
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHarm reductionEnvironmental planningReduction (mathematics)Sustainable developmentHarmDrugPolitical scienceMedicineGeographyPharmacologyLawPublic healthNursing
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores the conceptual and practical relationship between the goals of sustainable development and of contemporary drug policy in Canadian provinces and municipalities. By failing to address the global issue of substance abuse, particularly in urban contexts, the sustainable development community is likely unable to achieve many of its substantial social and ecological objectives. In Canadian cities, the abuse of recreational drugs has had deep social, economic and ecological consequences that have been accelerated, or in some cases created, by traditional prohibition-based approaches to drug policy. However, a more recent policy approach may provide a viable opportunity for sustainability scholars to engage with and help address this issue: the harm reduction approach. \nThis thesis focuses on the exploration of this opportunity by identifying overlapping principles and objectives that exist between harm reduction and sustainable development discourses. I review the literatures of harm reduction and sustainable development in order to identify common principles and historical experiences that could help to create a foundation for future collaboration. Emphases on social justice, social cohesion, community wellbeing and quality of life are identified as shared objectives throughout the literature. In addition, through the application of a sustainability assessment tool, several Canadian federal, provincial, and municipal harm reduction policy documents (HRPD) are shown to implicitly address a variety of sustainability concerns. I find that the HRPDs maintain a focus on a multitude of issues relevant to sustainability such as health, access to services, and democratic governance. Nevertheless, the results show that these documents largely fail to engage with ecological concerns relevant to drug policy, presenting an opportunity for learning and policy improvement in future iterations through the incorporation of a sustainability perspective. By understanding these synergies and disparities, future policy can be engaged to create co-beneficial, cross-discipline outcomes that help make progress toward more socially and environmentally sustainable communities, with an emphasis on wellbeing, inclusion and social justice. \nOther contributions of this research relate to the identification of potential bridging concepts, drawing from theories in geography, environmental governance and environmental justice. The identification of these concepts enables future research to be conducted and may be able to facilitate the translation of knowledge between the currently disparate literatures. Furthermore, recommendations are offered to both policy makers and practitioners that would help to make progress toward the goals of sustainable development as well as those of harm reduction. Overall, the findings of this research offer practical and theoretical contributions that serve to help address a pressing global issue, and in doing so identify substantial directions for new research.

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)0.014
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.220
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0270.103
Scholarly communication0.0260.017
Open science0.0040.023
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0050.001

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 head0.072
GPT teacher head0.351
Teacher spread0.279 · 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 designQualitative
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

Citations0
Published2019
Admission routes1
Has abstractyes

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