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Record W7005260835

A preliminary human rights-based analysis of Winnipeg's municipal budget

2024· dissertation· en· W7005260835 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicPublic health and occupational medicine
Canadian institutionsnot available
FundersStrong
KeywordsPrioritizationCultural rightsPoliticsHuman resourcesCapital (architecture)Human rightsService (business)
DOInot available

Abstract

fetched live from OpenAlex

Each year, the City of Winnipeg formulates an operating and capital budget for municipal-ran services, including Water and Waste, Fire Paramedic Services, Community Services, Property and Development, City clerks, and the Police Service. The two departments that receive the most money in the tax-supported operating budget are the police services and public works despite community demands in Winnipeg for more resources towards libraries, public washrooms, transit, and housing due to its declining conditions. The continued prioritization of these services poses a question: are economic, social, and cultural rights (ESCR) priorities of the City of Winnipeg? Article 2(1) of the International Covenant on Economic, Social, and Cultural Rights (ICESCR) obliges States to use its maximum available resources to progressively realize ESCR. Using the ICESCR’s Article 2(1) framework, the study conducts a preliminary human rights-based budget analysis of Winnipeg’s municipal budget from 2020 to 2024. The findings reveal that, in addition to the disproportionate allocation of resources between services benefitting civil and political rights (CPR) and ESCR that prioritizes the former than the latter, the City of Winnipeg is failing to utilise its maximum available resources to progressively realize the ESCR under Community Services by underspending resources already adopted for the department.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes1
Has abstractyes

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