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Record W2118184400 · doi:10.1002/job.508

The contours and consequences of compassion at work

2008· article· en· W2118184400 on OpenAlexaff
Jacoba Lilius, Monica C. Worline, Sally Maitlis, Jason Kanov, Jane E. Dutton, Peter J. Frost

Bibliographic record

VenueJournal of Organizational Behavior · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsCompassionWitnessNarrativePsychologySensemakingVariety (cybernetics)Social psychologyCompassion fatigueWork (physics)Clinical psychologyBurnoutPublic relationsPolitical scienceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract This paper describes two studies that explore core questions about compassion at work. Findings from a pilot survey indicate that compassion occurs with relative frequency among a wide variety of individuals, suggesting a relationship between experienced compassion, positive emotion, and affective commitment. A complementary narrative study reveals a wide range of compassion triggers and illuminates ways that work colleagues respond to suffering. The narrative analysis demonstrates that experienced compassion provides important sensemaking occasions where employees who receive, witness, or participate in the delivery of compassion reshape understandings of their co‐workers, themselves, and their organizations. Together these studies map the contours of compassion at work, provide evidence of its powerful consequences, and open a horizon of new research questions. Copyright © 2008 John Wiley & Sons, Ltd.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.232
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations502
Published2008
Admission routes1
Has abstractyes

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