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Record W1895624484 · doi:10.1108/14777280810896390

A framework for team coaching: using self‐discrepancy theory

2008· article· en· W1895624484 on OpenAlex
Davar Rezania

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDevelopment in Learning Organizations An International Journal · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCoachingTeamworkOriginalityTeam effectivenessPsychologyValue (mathematics)Knowledge managementTeam compositionTeam learningPsychological safetyIdeal (ethics)Applied psychologyComputer scienceSocial psychologyManagementPedagogyTeaching methodCooperative learning

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to provide a framework for project managers and team coaches to help them coach their team. Design/methodology/approach A team is provided with a measurement of actual experience of teamwork juxtaposed with its ideal experience of teamwork. Findings The paper finds that self‐discrepancy theory helps us explain that this triggers self‐directed learning in the team. Research limitations/implications A more comprehensive picture of team learning that takes into account non‐measurable dimensions of interaction might be of value in a framework for team coaching. Practical implications Using such a framework, a team leader can help team members to develop both their individual and team skills. This would create far more knowledgeable and skilled individuals who can contribute to an organization within their sphere of influence. Originality/value This paper extends current methods of coaching and offers practical help to team coaches.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.482
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.271
Teacher spread0.247 · 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