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Record W2906587468 · doi:10.1177/0734371x18816139

Caught Between Volunteerism and Professionalism: Support by Nonprofit Leaders for the Donative Labor Hypothesis

2018· article· en· W2906587468 on OpenAlex
Mirae Kim, Étienne Charbonneau

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Public Personnel Administration · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsNonprofit sectorProfessionalizationCognitive reframingNarrativeMeaning (existential)Labour economicsWorkforcePublic relationsSurvey data collectionEconomicsBusinessSociologyPolitical scienceSocial psychologyPsychologyEconomic growth

Abstract

fetched live from OpenAlex

The rise of professionalism within the nonprofit sector has transformed the sector’s reliance on well-meaning volunteers to paid professionals. While the professionalization of the nonprofit workforce is likely to continue, nonprofits are increasingly challenged for their inability to pay competitive wages. Our study argues that a social expectation for nonprofit employees to forgo some of their wages influences the donative labor narrative, which in turn impacts low nonprofit wages. We present data from an online survey experiment of executive directors at 467 nonprofits, along with their organizations’ Form 990 filings, to contrast socially biased attitudes and genuine views toward the donative labor hypothesis. The findings illustrate that the donative labor narrative should be understood as a result of social expectations for sacrifice of nonprofit employees, rather than a simple outcome of supply and demand in the labor market. We discuss the need to reframe the widespread donative labor narrative.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.132
GPT teacher head0.392
Teacher spread0.261 · 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