Shifting Sands: The Institutional Embeddedness of the Sydney Nonprofit Sector and its Relation to the State, Market, and Civil Society
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".