Cohesion, Productivity Norms and Effectiveness of Small Production Groups and Informal Subgroups
Bibliographic record
Abstract
The relationship of cohesion and productivity norms (including their combination with each other) to perceived subject-activity effectiveness (fulfillment of the plan and current tasks, success of activity in difficult conditions) and socio-psychological effectiveness (group/subgroup satisfaction, psychological comfort in the group/subgroup) of small production groups and informal subgroups has been studied. The study was conducted among 39 production groups (N=349 employees) in different occupational areas, most of which were dominated by joint-individual forms of organization. All of the surveyed groups were found to have stable informal subgroups, the number of which varied from one to three. Cohesion within groups and subgroups was positively and significantly more strongly related to socio-psychological effectiveness than to subject-activity effectiveness. This relationship is stronger in groups compared to subgroups. The subgroups’ norm of productivity has a significant positive correlation with their fulfillment of the plan and current tasks. No significant correlation was found between the groups’ productivity norm and any indicator of their subject-activity effectiveness. The norm of productivity and cohesion creates a positive interactive effect regarding the success of the groups in difficult conditions and the subgroups’ fulfillment of plan and current tasks. The findings extend the understanding of the direct and mediated links between cohesion and productivity norms and the two types of effectiveness of small production groups and the informal subgroups formed in them.
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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 | 0.011 |
| 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 | 0.001 |
| 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".