Rethinking Narcissism in Sports: Advantageous or Detrimental to a Team’s Success?
Bibliographic record
Abstract
Drawing on literature from personality and social psychology, the role of narcissism continues to be one of the most controversial constructs in sport. Reviewing a broad array of empirical research, this paper aimed at unravelling the effects of narcissism in various sporting elements: the players, the coaches, and the fans. Exploring both the benefits and drawbacks of narcissism, research reveals that narcissism can be valued as either advantageous or detrimental, depending on the component of the game. Reflecting upon the players aspect, narcissistic athletes might be best suited for individual sports as they provide the greatest opportunity for self-enhancement. Simultaneously, while narcissistic players often create major problems for a team, teams must not steer clear of all narcissistic athletes as they seem to be the most dependable performers during “big” games. Shifting towards coaches, research finds that those who exhibit moderate levels of narcissism, maximize leadership effectiveness such as communicating goals successfully with others. Yet, while these ideal levels may contribute to team success, research continues to find that narcissistic coaches severely impact team well-being as they are unable to put the needs of the team above theirs. Finally, despite the aggressiveness expressed by highly identified fans, the benefits of collective narcissism certainly prevail as their powerful presence leads their team to more successful outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".