Bibliographic record
Abstract
Drawing on our experiences in developing a new governance model for the Canadian Health Network, in this paper we argue that there is currently no agreement about a prescriptive or ideal model of non-profit governance. Rather we suggest that within the current diversity of thought about governance there is an exciting opportunity to create new models which are hybrids of existing and emerging models with the selection of the best model based on a contingency approach. The paper begins with a review and critique of the normative and academic literatures on non-profit boards looking at the assumptions which inform each. The paper then characterizes existing governance models along two dimensions: established vs. innovative and unitary vs. pluralistic. This provides us with a way of mapping current perspectives according to four different models; the Policy Governance model, the Entrepreneurial model, the Constituency model and the Emergent Cellular model. The paper briefly describes the characteristics of each model and outlines the positive and negative features of each. The paper concludes by describing a new hybrid model which embraces the strengths of each model and also capitalizes on some of the new ways of framing management in turbulent times.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".