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Record W2152596324 · doi:10.1007/s11266-012-9274-0

Perceptions of Board Chair Leadership Effectiveness in Nonprofit and Voluntary Sector Organizations

2012· article· en· W2152596324 on OpenAlexaffabout
Yvonne Harrison, Vic Murray, Chris Cornforth

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of VictoriaYork University
Fundersnot available
KeywordsPerspective (graphical)Construct (python library)TurnoverPerceptionEmpirical researchPublic relationsLeadership studiesSociologyPolitical sciencePsychologyManagementLeadership styleEconomics

Abstract

fetched live from OpenAlex

Abstract This article reports on the results of a multi-year, multi-phase international quantitative research investigation into perceptions of board chair leadership impact in nonprofit and voluntary organizations in Canada, the United States, and the United Kingdom. Specifically, this research tests four hypotheses and a hypothesized model derived from theoretical perspectives on chair leadership effectiveness that emerged when the results of a prior grounded theory research investigation were reviewed ex post facto through the lens of leadership literature (see Harrison and Murray, NPML, accepted). The purpose of this phase of the research is to determine: (a) whether there is empirical support for the theoretical perspectives advanced; and (b) which perspective offers the best explanation for why some board chairs are perceived as having more impact in the role than others. The results suggest chair leadership effectiveness is best understood as a multi-dimensional theoretical construct explained by more than one leadership theory. The article concludes with a discussion of the findings and directions for further research.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.298
Teacher spread0.276 · 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

Citations33
Published2012
Admission routes2
Has abstractyes

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