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Record W2038237923 · doi:10.1108/01437730710761742

Leadership development: learning from best practices

2007· article· en· W2038237923 on OpenAlexaff
Sheri‐Lynne Leskiw, Parbudyal Singh

Bibliographic record

VenueLeadership & Organization Development Journal · 2007
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsYork University
Fundersnot available
KeywordsLeadership developmentBest practiceOriginalityKnowledge managementValue (mathematics)Training and developmentVariety (cybernetics)Management scienceComputer scienceProcess managementBusinessPublic relationsManagementSociologyPolitical scienceEngineeringQualitative research

Abstract

fetched live from OpenAlex

Purpose The main purpose of this paper is to conduct a systematic review of the literature on best practices and propose a series of steps or practices that practitioners can use in developing and assessing their leadership development strategies and programs. Design/methodology/approach This is a review paper. An extensive literature review was conducted (by searching texts and business databases, such as ABIInform/Proquest, for “leadership development best practices”). Once an organization was identified, several criteria were used to decide whether it would be included in this study: independent analysts classified the practice as “best” in the leadership development area; leaders were “made” through integrated, multi‐mode programs that included top management support, systematic training, etc. Findings Six key factors were found to be vital for effective leadership development: a thorough needs assessment, the selection of a suitable audience, the design of an appropriate infrastructure to support the initiative, the design and implementation of an entire learning system, an evaluation system, and corresponding actions to reward success and improve on deficiencies. Research limitations/implications The paper identified “best practice organizations” by reviewing the literature. While this is an acceptable method, it resulted in wide range of determining criteria. Practical implications The most important implication of this paper is practical in nature. Essentially, organizations can use the six stages identified in the paper to help them develop and implement effective leadership development strategies. Originality/value Leadership development has become a key strategic issue for contemporary organizations. There is considerable evidence to suggest that organizations that do not have properly structured leadership development processes compete in the marketplace at their own peril. Several organizations have reported successes with particular approaches, yet an examination of the literature reveals that the lessons emanating from these success stories are generally not presented in a holistic manner. This is the need that we address in this paper.

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)0.219
metaresearch head score (Gemma)0.288
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.219
Threshold uncertainty score0.963

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2190.288
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0220.016
Science and technology studies0.0050.013
Scholarly communication0.0220.025
Open science0.0070.012
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0040.002

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 head0.335
GPT teacher head0.369
Teacher spread0.034 · 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.

Study designNot applicable
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

Citations182
Published2007
Admission routes1
Has abstractyes

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