Humanizing, surprising, controlling, and caring: emotion work and emotional labor strategies in sport for development and peace
Bibliographic record
Abstract
In the sport for development and peace (SDP) field, emotions play a key role. The purpose of this study was to examine how SDP practitioners in organizations serving refugees use emotion work and emotional labor strategies that seek to manage their own and other’s emotions. The conceptual framework guiding this study brings together literature on emotion work and emotional labor. A qualitative research design was used, and 14 semi-structured interviews were undertaken from May to July 2023 with SDP practitioners (e.g. program directors, founders, project managers) from organizations located in North America, Africa, Europe, Asia, and South America. Data analysis led to the construction of three themes, including: (1) humanizing in storytelling; (2) controlling emotions in the management of SDP implementation; and (3) caring. Humanizing in storytelling sought to challenge people’s assumptions about the refugee communities SDP organizations work with. Controlling emotions refers to the emotional labor and work that practitioners engage in to manage their own feelings when working with refugees. Caring for how refugees are represented and acknowledging emotions in SDP implementation underpins the first and second themes. This study advances a deeper understanding of the reflexive, purposeful work SDP practitioners undertake to ensure care for and of refugees. Theoretically, the study demonstrates how the emotional labor of individuals connects with and plays a role in emotion work carried out at an organizational level. SDP practitioners should consider both staff and program participant’s emotions and include training in emotional regulation for programs.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".