‘Misfit’ and ‘jack of all trades’: A qualitative exploration of the structure and functions of a network administrative organisation in Ontario, Canada
Bibliographic record
Abstract
ObjectivesLarger, more complex inter-organisational networks with strong, centralised governance structures, often in the form of a network administrative organisation (NAO), have developed in recent years in response to wicked health and social problems. Set in Ontario, Canada, this study explored how NAOs and networks are structured, how they function, and how they evolve.MethodsWe conducted a case study of a NAO and network consisting of 40 member networks in the province of Ontario, Canada. We analysed secondary sources, including policy documents, legislation, contracts, websites, and existing qualitative data.ResultsThe NAO and member networks developed in tandem and dialectically. They ultimately took on a form that defies categorisation within the existing literature due to their structure as a 'network of member networks' and by acting simultaneously as a policy network, service delivery coordination network, and governance network, by executing numerous complex mandates and functions in service of multiple stakeholders, and by exemplifying both high control and high collaboration.ConclusionsWe classified the NAO and its network as a 'misfit' and 'jack of all trades'. These features may help explain its perceived effectiveness. The complexity and hybrid nature of the NAO and network may position it to best address multifaceted health care problems.
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 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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".