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Record W4200592266 · doi:10.1177/08997640211057433

Nonprofit Financial Response to Immigration

2021· article· en· W4200592266 on OpenAlexfundno aff
Claire Le Barbenchon, Lisa A. Keister

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsImmigrationNonprofit sectorRevenuePopulationBusinessService (business)Demographic economicsEconomic growthPublic relationsFinancePolitical scienceEconomicsSociologyMarketing

Abstract

fetched live from OpenAlex

Nonprofit organizations are important actors in local communities, providing services to vulnerable populations and acting as stewards for charitable contributions from other members of the population. An important question is whether nonprofits spend or receive additional revenues in response to changes in the populations they serve. Because immigrant populations both receive and contribute to nonprofit resources, changes in immigrant numbers should be reflected in changing financial behavior of local nonprofits. Using data from the National Center for Charitable Statistics and the American Community Survey, we study whether nonprofit financial transactions change in response to changes in the local immigration population, the nature of the change, and the degree to which these changes vary by nonprofit type. Findings suggest that nonprofit financial behavior changes with growth and decline in immigrant populations underscoring the importance of nonprofits as service providers and contribute to an understanding of how organizations respond to external forces.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.014
GPT teacher head0.276
Teacher spread0.262 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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