Bibliographic record
Abstract
Purpose The purpose of this article is to discuss the implications of three lessons that leaders can learn from Canada geese to leadership and teamwork in organizations. Design/methodology/approach Migratory behavior of Canada geese is compared to widespread behavior among leaders and teams in organizations. Findings The first lesson is: work as a team: Canada Geese migrate long distances flying in V‐formation. This formation results in lesser wind resistance, which allows the whole flock to add around 70 percent greater flying range than if each bird flew alone. Geese find out quickly that it pays handsomely to be team players. Second, wise leadership: when the leader at the apex of the V gets tired, it is relieved by another goose. Leaders rotate, empower, delegate, and even step down when it's in the best interest of the team. How often do we see this taking place among organizational leaders? Wise leaders ensure that their followers are well trained and developed in order to achieve true empowerment and smooth succession processes. Third, humane behavior: if a goose drops to the ground when it gets hurt or sick, two of its colleagues go down with it to take care of it until it either gets healthier or dies. In this fast‐paced and competitive age, we seldom see managers going out of their way to help colleagues who are in trouble. In organizations, morale, productivity, and loyalty increase when employees are treated humanely. Originality/value This paper discusses ways that leaders, teams, and organizations can improve performance by applying three lessons learned from Canada geese.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".