Bibliographic record
Abstract
Purpose This paper aims to illustrate the interesting parallels that exist between the social behavior of farm animals and work‐teams as a means of offering a novel point of view from which to understand teams. Design/methodology/approach The empirical literature on the social behavior of farm animals is used as a starting‐point to identify the key factors that generate instability in social relationships and, as a result, demonstrate the existence of a dominance hierarchy. These factors are then analyzed in the context of the equivalent workplace literature in the areas of teamwork, bullying, and democracy. Findings There may be more of a connection between democracy in a barn and in a workplace than most people think. Both farm animals and humans have a tendency to form fairly stable social structures characterized by a dominance hierarchy in which there is: an established pecking order, differential access to resources; hazing of new members; penalties for non‐conformance; and a lack of personal space. Practical implications These factors contribute to instability in social relationships in the workplace and serve as potential explanations for the extent of aggression and incivility seen in today's organizations. Team leaders should pay attention to the markers of a dominance hierarchy and attempt to involve employees in democratic, participative work processes. Originality/value Team leaders will find this paper an unusual but effective way of considering how their teams are functioning.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".