How Coaching Contributes to the Development of Leadership
Bibliographic record
Abstract
It is not "business as usual " in the Voluntary Sector any more. Leading a sound and thriving organization that responds to community needs is still essential, and it is no longer enough. Now, executive directors are expected to stimulate wider community change. A large proportion of today's voluntary sector leaders ' time is devoted to larger issues such as community safety, harm reduction, prevention of early school leaving, youth suicide, homelessness. A leader's field of action and alliances does not stop at the walls of their agency, but extends even beyond their sector and to other parts of the country. While there are a number of core competencies that are foundational to leading a sound organization, there are many skills that can only be learned "on the fly " in response to changing conditions---- skills like: negotiating with and creating enrollment within large bureaucracies like school boards, various levels of government; maintaining relationship while working positively with conflict; skillfully dealing with differences in culture such that these differences contribute to strength. The Voluntary Sector Initiative, a national effort sponsored by the Government of Canada and the voluntary sector, identified a range of competencies that today's executive director needs. Table One summarizes these competencies (see appendix).
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".