Bibliographic record
Abstract
Research question This paper questions why, despite capitalism’s intimate connections with sport, it is rarely named, let alone explicitly discussed in sport management. It questions whether capitalism should remain as the invisible, ghostly backdrop wherein sport management is located and conceptualized.Research methods This paper is primarily a position and conceptual paper, though it is foregrounded with a search of the term ‘capitalism’ within leading sport management academic journals, conference abstracts, and textbooks. It also provides a synopsis of capitalism (as a global system of power) and suggests that capitalism has a ghostly presence in contemporary sport management scholarship.Results and findings This paper advocates for an expansive understanding of ‘sport management’ as the organizing processes of sport activities, as opposed to the ‘managing’ modalities with capitalist values. Naming capitalism is a necessary first step for sport management research to become more accountable to social justice and emancipation.Implications Naming capitalism makes it analyzable. It opens up intellectual space to support multi-racial, multi-gender working-class and anti-colonial struggles within and beyond the sport industry, furthering existing analyses on racism, sexism, heteronormativity, ableism, etc. with a renewed focus on contradictions under capitalism. Moreover, it opens up possibilities to theorize non-capitalist forms of organizing sport that challenge the default logics of the sport ‘industry’.
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 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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".