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Record W4389993581 · doi:10.1007/s11266-023-00628-1

The Ideological Work of Philanthropic Myths: A Study of Post-pandemic Disbursement Quota Reforms in Canada

2023· article· en· W4389993581 on OpenAlexaffabout
Fahad Ahmad, Adam Saifer

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaToronto Metropolitan University
Fundersnot available
KeywordsMythologyIdeologyDisbursementContext (archaeology)Government (linguistics)SociologyPosition (finance)Equity (law)Public administrationPolitical scienceEconomicsPolitical economyLawPoliticsFinanceGeography

Abstract

fetched live from OpenAlex

Abstract This research examines the role that myths play in sustaining the institutional position of philanthropy in a context of sector reinvention during the COVID-19 recovery. Specifically, we study discourse around the post-pandemic philanthropic sector reforms to the Disbursement Quota (DQ) in Canada. The DQ is the minimum asset payout rate that philanthropic foundations in Canada must maintain to enjoy charitable status and associated tax benefits. We examine submissions to government, media articles, and public statements by philanthropic sector advocates to analyze the ideological work of DQ-related discourses in creating and entrenching philanthropic myths. Our findings coalesce around three philanthropic myths: (1) the Modernization Myth (2) the Goodness Myth ; and (3) the Equity Myth . We argue that these philanthropic myths function to maintain the institutional position of philanthropy in this moment of sector reinvention by obscuring the sector’s colonial-capitalist histories and institutional contradictions.

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)0.001
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 score0.345
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.296
Teacher spread0.280 · 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.

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

Citations6
Published2023
Admission routes2
Has abstractyes

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