MétaCan
Menu
Back to cohort

Technology Enhanced Collaborative Leadership Development

2009· book-chapter· en· W2506581574 on OpenAlexaffabout
Beverly‐Jean Daniel, April Boyington Wall

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork University
Fundersnot available
KeywordsPresentation (obstetrics)Leadership developmentKnowledge managementThe InternetEngineeringProcess (computing)Engineering managementNeuroleadershipBusinessProcess managementComputer scienceLeadership studiesLeadership stylePublic relationsPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This chapter presents a case study of the process of employing technology in a project involving the development and presentation of a unique leadership program for the not-for-profit sector in a major Canadian city. The project relied on telephone and Internet technology as a primary means of communication between the three women developing and delivering this program. The chapter provides a background on the development of the program; the ways in which technology was employed; and the problems and benefits of employing technology in doing this. Finally, it identifies the strategies and interpersonal skills found to be most effective in facilitating technology-enhanced collaboration, and makes recommendations for maximizing the benefits of using technology in the process of creating new approaches to leadership development. The chapter can contribute to the literature in the field of leadership development, collaborative program development and diversity management in the field of leadership.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.004
Open science0.0020.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.003

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 head0.051
GPT teacher head0.292
Teacher spread0.241 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueIGI Global eBooksSame topicKnowledge Management and SharingFrench-language works237,207