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Record W2775144382 · doi:10.1002/nvsm.1607

Understanding solicitation: Beyond the binary variable of being asked or not being asked

2017· article· en· W2775144382 on OpenAlexaffabout
Beth Breeze, Gloria Jollymore

Bibliographic record

VenueInternational Journal of Nonprofit and Voluntary Sector Marketing · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsMount Allison University
Fundersnot available
KeywordsCognitive reframingTransactional leadershipTransactional analysisIdentity (music)PsychologyPublic relationsSocial psychologyQuality (philosophy)Political science

Abstract

fetched live from OpenAlex

The identity, motivation, and experiences of philanthropists have become increasingly popular topics of study in a wide range of disciplines, yet no equivalent attention has been paid to the askers, despite research showing that almost all donations are solicited in some way. The propensity to be asked for contributions has been found to be positively related to the propensity to give, but despite the usefulness of this finding, it reinforces the suggestion that solicitation is a binary variable, such that people are either asked or they are not asked. This paper, drawing on data from in‐depth interviews with 73 successful fundraisers in the UK and Canada, highlights the importance of the quality, as opposed to simply the quantity, of solicitation. Three important factors that lie behind successful “asks” are identified and discussed: First, they are made within relationships of trust rather than as a result of a transactional approach. Second, they occur as a result of fundraisers' ability to be an “honest broker” between donors and the organisations they might support. And third, they rely on the fundraisers' skills in reframing complex issues and finding alignment between the recipient organisation's needs and the philanthropic aspirations of the donor. The paper concludes with implications for practice.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.082
GPT teacher head0.334
Teacher spread0.252 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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