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Record W7100286213

How Coaching Contributes to the Development of Leadership

2015· article· en· W7100286213 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicCoaching Methods and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingThrivingVoluntary sectorGovernment (linguistics)Action (physics)Active listeningNegotiation
DOInot available

Abstract

fetched live from OpenAlex

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).

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.008
Scholarly communication0.0140.006
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.005

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.

Opus teacher head0.338
GPT teacher head0.408
Teacher spread0.070 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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