Bibliographic record
Abstract
List of Contributors. 1. Introducing the Emotional Organization: Stephen Fineman (University of Bath). Part I: Emotional Arenas. The Hospital. 2. Me, Morphine and Humanity: Experiencing the Emotional Community on Ward 8: Sharon Bolton (The University of Strathclyde). The Prison. 3.Power, Paradox, Social Support and Prestige: A Critical Approach to Addressing Correctional Officer Burnout: Sarah Tracy (Arizona State University). Crisis Work. 4. Rape Work: Emotional Dilemmas in Work with Victims: Patricia Yancey Martin, Douglas Schrock, Margaret Leaf and Carmen Von Rohr (Florida State University). The Recreation Centre. 5. In the Gym: Peer Pressure and Emotion Management Among Co-Workers: Mary Haman and Linda Putman (Texas A&M University). The Job Centre. 6. Abuse, Violence and Fear on the Front Line: Implications for the Rise of the Enchanting Myth of Customer Sovereignty: Marek Korczynski (Loughborough University) and Victoria Bishop (The University of Manchester). The Call Centre. 7. Enactments of Class and Nationality in Transnational Call Centres: Kiran Mirchandani (University of Toronto). Web Work. 8. The Gendering of Emotions and Perceived Work Time: Chicks and Geeks at I.com: Nicole L. Kangas and Debra E. Meyerson (Stanford University). Homeworking. 9. Managing the Boundaries of Telework: Gill Musson (Sheffield University) and Katy Marsh (Newcastle University Business School). Consultancy. 10. Management Consultancy and Humour in Action and Context: Andrew Sturdy (The University of Warwick), Timothy Clark (Durham University, Robin Fincham (Stirling University) and Karen Handley(Oxford Brookes University). Part II: Shifting Identities. 11. Becoming a Successful Corporate Character and the Role of Emotion Management: Caroline Hatcher (Queensland University of Technology). 12. Gender and the Emotion Politics of Emotional Intelligence: Stephanie A. Shields (Pennsylvania State University) and Leah R. Warner (Pennsylvania State University). 13. Feeling Out of Place? Towards the Transnationalizations of Emotions: Jeff Hearn (Swedish School of Economics and Business Administration). 14. It's All Too Beautiful: Emotion and Organization in the Esthetic Economy: Philip Hancock (The University of Warwick) and Melissa Tyler (Loughborough University). Epilogue: Stephen Fineman (University of Bath). Index
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".