A Bounded Emotionality Perspective on the Individual in the Organization
Bibliographic record
Abstract
Traditionally, books on organizational behavior proceed in a linear fashion from the individual, to groups, and then to the organization as a whole. This volume is no exception. Organizations exist only because of the people within them. Consequently, understanding organizations first of all requires understanding the people who populate them, and especially their needs, drives, and capabilities. It is all the more surprising therefore to find that organizational behavior scholars, and behavioral science researchers in general, were so slow to appreciate the centrality of emotion in organizations (see Ashforth & Humphrey, 1995). Indeed, even today, many scholars are reluctant to accept an emotions-oriented explanation of motivation and behavior (e.g., Becker, 2003). Interestingly, and as Weiss and Brief (2002) pointed out, early organizational behavior scholars were deeply interested in the role of feelings and emotion, but this research seems to have withered with the rise of behaviorism in the 1940s and 1950s.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".