MétaCan
Menu
Back to cohort
Record W4387969998 · doi:10.32721/ctj.2023.71.3.devlin

Government Funding of Charities Serving Indigenous Peoples

2023· article· en· W4387969998 on OpenAlexvenueaboutno aff
Rose Anne Devlin, Michela Planatscher

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGovernment (linguistics)RevenueAgency (philosophy)PopulationBusinessEconomic growthPublic administrationFunding AgencyPolitical scienceFinancePublic relationsEconomicsMedicineEnvironmental healthSociology

Abstract

fetched live from OpenAlex

There are several reasons why governments fund charities. Relative to government ministries, charities are often better able to assess and adapt to local needs, serve vulnerable populations, and deliver culturally sensitive services where appropriate. This article investigates the funding decisions of governments by focusing on charities that provide services to Indigenous individuals. The authors use Canada Revenue Agency T3010 data on registered charities from 2003 to 2017 to extract information on charities that serve the Indigenous population and further separate this group into those located off and on reserves. Governments fund Indigenous-serving charities differently than their non-Indigenous counterparts. Being an Indigenous-serving charity is associated with a 25 percent increase in the predicted probability of receiving government support relative to non-Indigenous charities (for the reference group). Indigenous-serving charities on reserve are 17 percent <i>less likely</i> to receive public funding relative to those off reserve. Federal government funding seems to act as a catalyst for provincial and municipal funding. The authors' results lend support to the idea that governments fund charities to provide locally appropriate services to vulnerable populations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.245
Teacher spread0.222 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Tax Journal/Revue fiscale canadienneSame topicIndigenous Health, Education, and RightsFrench-language works237,207