Research on Emotion in Organizations
Bibliographic record
Abstract
Citation (2011), "Research on Emotion in Organizations", Härtel, C.E.J., Ashkanasy, N.M. and Zerbe, W.J. (Ed.) What Have We Learned? Ten Years On (Research on Emotion in Organizations, Vol. 7), Emerald Group Publishing Limited, Bingley, p. iii. https://doi.org/10.1108/S1746-9791(2011)0000007022 Publisher: Emerald Group Publishing Limited Copyright © 2011, Emerald Group Publishing Limited Book Chapters What Have We Learned? Ten Years On Research on Emotion in Organizations Research on Emotion in Organizations Copyright Page Dedication List of Contributors About the Editors Overview: What Have We Learned? Ten Years On Chapter 1 Synthesizing What We Know and Looking Ahead: A Meta-Analytical Review of 30 Years of Emotional Labor Research Chapter 2 Understanding the Relationship between Emotional Labor and Effort Chapter 3 Tricks of the Trade: Customer Service Employee Strategies in Performing Emotion Work Chapter 4 Sales Employee's Emotional Labor: A Question of Image or Support Chapter 5 The Role of Emotions in Supporting Independent Professionals Chapter 6 Coding Emotions in Complaint Behavior: Comparing the Shaver et al. and Richin's Consumption Emotions Sets Chapter 7 Affective Events Theory as a Framework for Understanding Third-Party Consumer Complaints Chapter 8 Display Rules and Emotional Labor within Work Teams Chapter 9 Emotional Intelligence as a Moderator of the Quality of Leader–Member Exchange and Work-Related Outcomes Chapter 10 Managing Negative Emotions in Emergency Call Taking: A Heat-Model of Emotional Management Chapter 11 The Measurement of Trait Emotional Intelligence with TEIQue-SF: An Analysis Based on Unfolding Item Response Theory Models Chapter 12 Exploring the Antecedent and Consequences of Authenticity of Emotional Expression Chapter 13 A Positive Approach to Workplace Bullying: Lessons from the Victorian Public Sector Appendix: Conference Reviewers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".