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Record W4285214604 · doi:10.1525/gp.2022.36383

Shifting Sands: The Institutional Embeddedness of the Sydney Nonprofit Sector and its Relation to the State, Market, and Civil Society

2022· article· en· W4285214604 on OpenAlexaff
Hokyu Hwang, Danielle Logue

Bibliographic record

VenueGlobal Perspectives · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsEmbeddednessCivil societyNonprofit sectorContext (archaeology)State (computer science)Government (linguistics)Political sciencePolitical economyPublic administrationBusinessPoliticsSociologyLawSocial science

Abstract

fetched live from OpenAlex

In this essay, we explore the institutional embeddedness of the Sydney nonprofit sector via its changing relations with the state, market, and civil society. We explore the historical development of these relations and how these durable relations have shifted in recent years, putting pressures on the sector. The federal government’s effort to constrain advocacy practices has resulted in a tense relationship between the sector and the state. The push to introduce market mechanisms to generate resources for the sector and the rise of impact investing have pushed nonprofit organizations to explore financial innovations and into the now locally labeled “social economy.” These developments directly impinge on how nonprofits perform their roles by circumscribing the scope for advocacy and by putting nonprofits on a different path for financial sustainability. Compounding these shifts are the COVID-19 pandemic and the sector’s relationship with civil society. The pandemic underscored the importance of the work carried out by nonprofits and saw a resurgence in the sector’s relationship to civil society, while revealing the sector’s chronic fragility. By examining the institutional embeddedness of the nonprofit sector in this way, we provide a common framework for understanding a local nonprofit sector in the context of global changes, fostering future comparative work.

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)0.000
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 score0.590
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.015
GPT teacher head0.274
Teacher spread0.258 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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