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Record W2528283070 · doi:10.1007/s11266-016-9801-5

‘Shopping for Change’: <i>World Vision Canada</i> and Consumption-Oriented Philanthropy in the Age of Philanthrocapitalism

2016· article· en· W2528283070 on OpenAlexafffundabout
Vincci Li

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDonationFraming (construction)Consumption (sociology)AdvertisingProduct (mathematics)MarketingProfit (economics)BusinessPublic relationsSociologyEconomicsPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

Abstract According to Charity Intelligence Canada, in 2014, at least 21 Canadian non-profits published a gift catalogue featuring a range of “products” a that donors could “purchase” for people in need. These charity gift catalogues, along with other shopping-inspired fundraising initiatives, represent a significant shift in the philanthropic arena towards philanthrocapitalism. Using two World Vision Canada campaigns as exemplars, this article offers a critical analysis of what the author calls “consumption-oriented philanthropy” b —a class of charitable giving that is heavily guided by market principles without involving a consumer product. Unlike purchase-triggered donation campaigns (in which a charitable donation is made when a consumer product is purchased), consumption-oriented philanthropy does not require the purchase of a for-profit commodity; instead, consumption-oriented philanthropy reformulates aid recipients or charitable aid itself into symbolic commodities. By re-framing charitable aid as a pseudo-shopping experience, however, consumption-oriented philanthropy ushers in an entirely different set of values, expectations, and logic that shapes the way in which donors understand and engage in philanthropic giving.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.319
Teacher spread0.295 · 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

Citations12
Published2016
Admission routes3
Has abstractyes

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