A preliminary human rights-based analysis of Winnipeg's municipal budget
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".