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Record W3010369136 · doi:10.17645/pag.v8i1.2505

Leadership as Interpreneurship: A Disability Nonprofit Atlantic Canadian Profile

2020· article· en· W3010369136 on OpenAlexafffundabout
Mario Levesque

Bibliographic record

VenuePolitics and Governance · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsMount Allison University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRetrenchmentPolitical sciencePublic relationsState (computer science)Face (sociological concept)Public administrationSociology

Abstract

fetched live from OpenAlex

The entrenchment of the neoliberal state and rise of populist leaders has marginalized the role of voluntary organizations in society. This presents significant challenges for nonprofit leaders in economically challenged areas as it erodes their ability to protect and serve vulnerable populations. Attention turns to maintaining hard fought gains at the expense of making progress. Yet doing so requires new skills and leadership styles to manage organizational change where innovation and transformation are key. Based on 42 qualitative interviews with disability nonprofit leaders in Atlantic Canada, our study aims to characterize this transformation. Using Szerb’s (2003) key attributes of entrepreneurship that distinguish between entre-, intra-, and interpreneurs, we find disability leaders have become interpreneurs. We find a strong emphasis on networked service delivery underscoring shared goals, risks and responsibilities, and resources. For disability leaders, cultivating relationships and strong communication skills are essential. In the face of populist desires for state retrenchment, we question how long this collective response can hold given ongoing economic challenges.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.680

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0250.003
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.091
GPT teacher head0.242
Teacher spread0.152 · 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 designQualitative
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

Citations8
Published2020
Admission routes3
Has abstractyes

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