MétaCan
Menu
Back to cohort
Record W653464979 · doi:10.1177/0734371x15587980

Money Talks or Millennials Walk

2015· article· en· W653464979 on OpenAlexaff
Jasmine McGinnis Johnson, Eddy S. Ng

Bibliographic record

VenueReview of Public Personnel Administration · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNonprofit sectorWorkforcePrivate sectorPublic sectorBusinessCompetition (biology)Public relationsValue (mathematics)Work (physics)PerceptionMarketingLabour economicsEconomicsEconomic growthPolitical sciencePsychology

Abstract

fetched live from OpenAlex

The nonprofit sector has become increasingly reliant on paid professional staff and now faces competition from the private and public sectors, which often pay higher to attract and retain workers. Although Millennials are attracted to nonprofit work, there are concerns that they will not remain committed to the nonprofit workforce due to low pay. We analyzed data from the 2011 Young Nonprofit Professionals Network Survey to examine the relationship between pay, perceptions of equitable pay, and sector-switching intentions among Millennial nonprofit workers. Although two thirds of the respondents indicate sector-switching intentions, we found no evidence that Millennial nonprofit workers, who are purported to value extrinsic and materialistic rewards, expressed sector-switching intentions on account of pay. However, pay influences the sector-switching intentions of Millennial nonprofit managers and those with advanced education. Our results suggest that the nonprofit sector may be facing challenges in attracting and retaining Millennial managers because of low pay.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0380.005

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 head0.171
GPT teacher head0.395
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations103
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueReview of Public Personnel AdministrationSame topicNonprofit Sector and VolunteeringFrench-language works237,207