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Record W4306154358 · doi:10.3138/uhr-2021-0003

Municipal Funding for the Non-profit Sector: A Methodology

2022· article· en· W4306154358 on OpenAlexaffvenueabout
Dominique Clement

Bibliographic record

VenueUrban History Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPoliticsScholarshipPublic administrationCorporate governanceProfit (economics)Not for profitCivil societyLocal governmentEconomicsBusinessEconomic growthPolitical scienceFinanceAccountingLaw

Abstract

fetched live from OpenAlex

Non-profit organizations have been an indelible feature of urban life in Canada since at least the nineteenth century. They have also, since the 1970s, come to rely heavily on state funding from all three levels of government. Yet scholarship on how the state has used its spending power to shape the non-profit sector is entirely focussed on provincial and federal policy. In part, this reflects the immense obstacles to collecting historical data on municipal funding. This article provides a methodology for collecting data on municipal funding for the non-profit sector. It is based on a study of 25 municipalities in British Columbia. For historians, this type of research offers unique insights into the policies and politics of municipal governance; the diversity of Canada’s non-profit sector; how urban communities have changed over time; the shifting dynamics in the relationship between the state and civil society; and how local governments use their spending power to shape the non-profit sector. It provides an opportunity to better understand the emergence a network of organizations that makes possible modern urban life.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.252
GPT teacher head0.348
Teacher spread0.096 · 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
GenreReview

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
Published2022
Admission routes3
Has abstractyes

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