Effectiveness of a Shared Leadership Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article applies and builds upon the network leadership models introduced by Provan and Kenis to the case of the British Columbia Network for Aging Research (BCNAR). We specify a particular type of shared leadership model and term this a Targeted Shared Leadership (TSL) model based on the governance structure of BCNAR. Key features include six coleaders who are selected on the basis of representation of five major universities (typically in its gerontology center) situated in the five provincial health authorities in British Columbia. Several network characteristics are introduced and then applied to BCNAR to assess effectiveness of the leadership structure. Innovations in research grant capacity support, communication, mentorship and training of new gerontologists, and knowledge translation are used to specify the effectiveness of the leadership structural dynamics of BCNAR. Potential applications of this shared leadership model for other networks are discussed.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it