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Record W7062487417

Tax increment finance: the legislative romance between the municipal government and Winnipeg stakeholders

2015· dissertation· en· W7062487417 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMspace (University of Manitoba) · 2015
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNucleofectionGestational periodTSG101DemotionHyporeflexiaHemopericardiumPretextArticular cartilage damage
DOInot available

Abstract

fetched live from OpenAlex

This research explores the effectiveness of Tax Increment Financing (TIF) as a financial tool to stimulate residential and commercial development in downtown Winnipeg. Cost is a commonly cited barrier to developing downtown, resulting in deteriorating and vacant city centers. TIF can remove some of the financial barriers to development, ultimately revitalizing downtown and benefitting the community as a whole. A comprehensive literature review was used to inform unstructured interviews with urban planners, developers, and various city officials to determine how TIF could be effectively implemented to revitalize Winnipeg’s downtown. Examining precedents from Winnipeg and across North America highlight the positive and negative impacts of TIF, as well as its promotion as a tool for urban renewal. TIF is an effective method of increasing property values, and encouraging development in priority areas. Tailoring present TIF methods to local conditions can help avoid increasing property taxes, wrongful use of land expropriation, or insufficient revenue generation.

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.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.036
GPT teacher head0.228
Teacher spread0.192 · 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