Group size and composition of work groups as precursors of intragroup conflicts
Bibliographic record
Abstract
PURPOSE: This study explores the connections between formal quantitative group characteristics (such as group size, group composition by gender, age, and duration of group membership of individual workers, their average age, and duration of membership) with three levels of conflict (ie, interpersonal, individual-group, and individual-subgroup) of two types (ie, activity-oriented and subject-oriented). METHOD: Forty-one work groups - small-size enterprises and basic-level teams and units in medium-size companies and large corporations, with the total sample of 334 individual workers - took part in the study. The study employed the questionnaire of interpersonal conflicts in a group and the questionnaire of individual-group and micro-group conflicts as assessment tools. Subsequent regression analyses explored the relationships between group size and composition on one hand and types and levels of conflict on the other. RESULTS: The study established that group size is negatively associated with the individual-subgroup subject-oriented conflict. Also, group size moderates the connections between several formal group characteristics and conflict types and levels. These connections are detected in large-size groups but are nearly nonexistent in small-size groups. Group diversity by gender is negatively associated with the individual-group activity-oriented conflict (across all participating groups) and with the interpersonal and individual-group subject-oriented conflicts (in large-size groups only). Group composition by duration of group membership is negatively associated with the individual-subgroup subject-oriented conflict (across groups), participants' average age and duration of group membership - with both types of the individual-subgroup conflict. Out of all group characteristics under consideration, only group composition by age was not associated with either of the conflict parameters. DISCUSSION: The paper makes a special point out of the fact that group characteristics served as much stronger predictors for conflict parameters in large-size groups than either in small-size groups or in the entire sample, indicating that the increase in group size strengthens the influence of group characteristics on conflict parameters. CONCLUSION: The research findings indicate that it is important, when studying connections between group composition and conflicts within the group, to take group size and its influence on types and levels of the intragroup conflict into account.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".