A Lesson in Effective Communication: An Interview With Dr. Gary Latham
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".