Nonprofit Leadership and the Nonprofit Quarterly Creating Community Governance: A View from the Inside
Bibliographic record
Abstract
Recently, Margaret Wheatley (2008) described the current times as an era of “powerful possibilities”. In that spirit, in this paper we reflect on efforts directed towards creating a system of community governance. We report on work that was done over the past few years by the board and executive of the Whitby Mental Health Center (WMHC) to become a catalyst for community governance within southern Ontario in Canada. To understand this model of governance Renz says we need to make two important conceptual shifts; (1) separating “governance ” as a function from the “board ” as a context or setting and (2) moving from a focus on the single free standing organization to a network of interorganizational alliances. Renz points out that he is not talking about “networked organizations ” but of “networks as organizations ” or non-hierarchical systems that link multiple constituents to work on matters of shared interest. The WMHC is a stand alone psychiatric hospital founded in 1919 and operated by the Ministry of Health until 2006 when responsibility for its management was transferred to a community based board of directors. WMHC provides a range of specialized, tertiary
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".