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Record W4403489393 · doi:10.1177/10525629241291234

A Lesson in Effective Communication: An Interview With Dr. Gary Latham

2024· article· en· W4403489393 on OpenAlexaff
Stephen D. Risavy, Gary P. Latham

Bibliographic record

VenueOrganizational Behavior Teaching Review · 2024
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsUniversity of TorontoWilfrid Laurier University
Fundersnot available
KeywordsPsychologyMedical educationVisual artsArtMedicine

Abstract

fetched live from OpenAlex

This interview-based article addresses the question of how management educators can increase their effectiveness in communicating with their students. A challenge for management educators who are looking to bring their teaching and impact to the next level is that there is a voluminous literature regarding effective communication best practices. The current article focuses on what management educators should prioritize to better engage their students, communicate with their students, and disseminate their work beyond academia. To help management educators determine which of the many effective communication practices should be focused on, the current article presents an interview with someone who is among the most influential scientist–practitioners in the field of management, Gary Latham. We advocate for management educators to set a specific, challenging goal for improving their teaching performance, and to also implement one or more of the following recommendations to communicate memorably when teaching management education students: (1) using everyday, layperson rather than scientific language; (2) emphasizing the value of behavioral science theory for practice; (3) explaining the danger of ignoring conditional variables; and (4) capturing and keeping students’ attention by presenting surprising findings.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.057
GPT teacher head0.450
Teacher spread0.393 · 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 teacher head, not a consensus.

Study designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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