The Role of Cohesion and Productivity Norms in Performance and Social Effectiveness of Work Groups and Informal Subgroups
Bibliographic record
Abstract
The study addresses the direct and indirect relationship of group cohesion and productivity norm with the perceived performance effectiveness (plan and current tasks implementation and performance success in challenging conditions) and social effectiveness (satisfaction with the group/subgroup and psychological comfort in the group/subgroup) at the levels of work groups and informal subgroups. Thirty-nine work groups from fifteen Russian organizations of different activity profiles, namely services, trade, and manufacturing, took part in the study. The vast majority of them were characterized by relatively low task interdependence. Within the work groups, informal subgroups (from one to three per group) were identified. The cohesion of groups and subgroups was positively and significantly stronger associated with their social effectiveness than with performance effectiveness. The cohesion of subgroups was also indirectly related to social effectiveness of the work groups, i.e., this association was mediated by the subgroup social effectiveness. The index of productivity norm was positively related to perceived performance effectiveness only at the subgroup level, but not at the group level. The productivity norm of the subgroups was also indirectly related to the perceived performance effectiveness of the groups, i.e., this association was mediated by the subgroup performance effectiveness. The indirect relationship between subgroup productivity norm and group performance effectiveness was more complex when cohesion within subgroups was taken 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 | 0.003 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".