Affective Primacy in Intraorganizational Task Networks
Bibliographic record
Abstract
To better understand the role of affect in organizational task-related networks, we developed a theory of affective primacy that identifies cognitive and motivational mechanisms through which the affective value of a social relationship (a feeling of positive affect from interactions with a colleague) operates as an antecedent of perceived instrumental value (a subjective evaluation of a relationship’s contribution to accomplishing assigned tasks). We tested this theory with full-network data collected over three years from employees in a small functional-form organization, which we analyzed with a methodology drawing from the social relations model of interpersonal perception and Bayesian models for social network analysis. We found that, over time, the affective value of social relationships influences both perceptions of instrumental value and the formation of task-related ties through multiple paths not accounted for by either perceived instrumental value or formal-structural requirements. We also show that the emergence of task-related networks rests primarily on high-activation positive emotions, such as excitement (a subjective state of feeling energized) rather than positive emotions with lower levels of activation, such as pleasantness (a subjective state of feeling gratified). We discuss implications of these findings for organizational theory and managerial practice.
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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 | 0.014 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".