Marxist or Feminist Approaches to Sports Management Are There Traits in A Modern Days Society
Bibliographic record
Abstract
Many scholars have attempted to apply various theories in the field of sport (Bordieu, 1984 & Brainer 2007). This particular area looks at the relationship between Marxism to the sociology of sport and how it has influenced societal structures as well as the impact it has had on the economy. Though these theories are useful on exploring the general nature of sport, questions may be raised on have they influenced the way sports is managed today also? It is widely accepted that management theories have been influenced by industry and that many scholars have used Marxism and feminist approaches to form some sort of construct of this. However does one or two apply to all? And are they appropriate to areas such as the service industry that sport falls in too? This paper attempts to look at how Marxism may have had some influence on sports management through capitalists, masculinity and power and the weld it has had on females developing in such a field because of its deeply held roots. And more importantly possibly oppressed feminism in this field. There are also arguments made because of such oppression by the male domination that they have used sports as a vehicle to segregate society and influence the direction of sports management. Therefore the discussion in its true entirety at most is a snapshot of how one theory dominates the area of sports management and how it impinges on others both on their application and development.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".