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Record W4366774169 · doi:10.29173/cjnser615

Do Service-Providing Nonprofits Contribute to Democratic Inclusion? Analyzing Democracy Promotion by Canadian Homeless Shelters

2023· article· en· W4366774169 on OpenAlexaffvenueabout
Anna Kopec, Kristin Pue

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCarleton University
Fundersnot available
KeywordsDemocracyPublic administrationCivil societyInclusion (mineral)Government (linguistics)PoliticsPromotion (chess)WelfarePolitical scienceDemocracy promotionPublic relationsSociologyDemocratizationSocial scienceLaw

Abstract

fetched live from OpenAlex

Nonprofits are key social service providers in many Western welfare states. Yet the nonprofits that deliver government-funded public services are also an important part of civil society and, in theory, promote democratic inclusion through their democratic civil society function. But to what extent do welfare-providing nonprofits carry out democracy-promoting activities in reality and what do these activities include? Using a survey distributed to Canadian charities that operate government-funded homeless shelters, we find evidence of activities falling within three areas of democracy promotion: support for political participation, internal democratic governance, and representative voice. The variation amongst different activities is presented in ideal types, which can inform future studies of the democratic function of nonprofits. Our empirical results point to a vital role of homeless shelters that extends beyond the provision of basic needs and contribute to a better understanding of the modalities of democratic inclusion for excluded 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0070.003
Scholarly communication0.0040.001
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.111
GPT teacher head0.431
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian journal of nonprofit and social economy researchSame topicHomelessness and Social IssuesFrench-language works237,207