The sociology of emotion in elite sport: Examining the role of normalization and technologies
Bibliographic record
Abstract
Recent research has examined the role of negative emotion norms and elite athletes’ decisions to continue to train in sport when they are not physically healthy enough to do so. According to Lee Sinden ((2010) The normalization of emotion and the disregard of health problems in elite amateur sport. Journal of Clinical Sport Psychology 4: 241–256; (2012) The elite sport and Christianity debate: Shifting focus from normative values to the conscious disregard for health. The Journal of Religion and Health), athletes are persuaded to accept negative perceptions of emotions through a process called “normalization of emotion”. The author draws on Foucault ((1977) Discipline and Punish: The Birth of the Prison. New York: Vintage Books) and Shogan ((1999) The Making of High-Performance Athletes: Discipline, Diversity, and Ethics. Toronto, ON: University of Toronto Press), to show how normalization works through an athlete’s emotions using methods of normalizing, including hierarchical observation, normalizing judgment, and the examination (Lee Sinden, 2010, 2012). The author discusses how negative perceptions of emotion, described as “technologies of emotion” (Lee Sinden, 2010, 2012), are a collection of historical myths that continue to work in elite sport to homogenize athletes’ outward emotional expressions. The consequence of normalization of emotion is the disassociation of athletes with their thoughts and feelings, and the potential subsequent suppression of concerns, such as training intensities and health. The following paper explores the theories of “normalization of emotion” and “technologies of emotion” in more detail. Through understanding how negative perceptions of emotions are socialized in sport, and the consequences of socializing emotions in this way, researchers and sport advocates may realize the need for a paradigm shift in the way elite sport understands and celebrates athletes’ diverse emotionalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".