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Record W2588308553 · doi:10.1017/s147474641700001x

The Prevalence and Distribution of High Salaries in English and Welsh Charities

2017· article· en· W2588308553 on OpenAlexfundno aff
John Mohan, Stephen McKay

Bibliographic record

VenueSocial Policy and Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
FundersEconomic and Social Research CouncilQueen's UniversityResearch Councils UK
KeywordsWelshSalaryReceiptDistribution (mathematics)Demographic economicsCommissionPopulationSample (material)PaymentBusinessLabour economicsAccountingEconomicsPolitical scienceDemographyFinanceGeographySociologyLaw

Abstract

fetched live from OpenAlex

There has recently been public discussion of the rewards available to senior staff in English and Welsh charities. However, that discussion is usually based on examples of individual salaries, or on unrepresentative and small subsets of the charity population. To provide a robust and informed basis for debate, we have conducted analyses of evidence on the payment of high salaries (defined as the numbers of people paid above £60,000 p.a., a reporting threshold used by the Charity Commission) in: (a) a representative sample of c.10,000 English and Welsh charities, and (b) surveys of individuals regarding comparative salary levels in different sectors of the economy. Overall, survey data show that the proportion of staff in receipt of high salaries is lower than average in the third sector than in other sectors. Information from charity annual accounts is used to demonstrate which charities are more likely than others to pay such salaries, and to relate the likelihood of paying high salaries to charity characteristics (income, location and subsector). We show that the distribution of high pay in the charitable sector is largely a function of the size and complexity of organisations, and is generally unrelated to subsector or income mix.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
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 head0.019
GPT teacher head0.313
Teacher spread0.293 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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