Building bridges: Connecting sport marketing and critical social science research
Bibliographic record
Abstract
Recently, sport management scholars have called for researchers to critically evaluate the ways in which research questions and resulting contributions truly disrupt what is known, how it is known, why it is important to know, and for whom. Historically, sport marketing research has adapted traditional research approaches from the parent marketing discipline to sport. Yet, sport is a constantly evolving social and cultural phenomenon and a reliance on conventional theories, concepts, and methods can serve to crystalize the discourse in sport marketing in ways that may limit knowledge production. Responding to this call, we believe that sport marketing research has much to gain from engaging with critical social science assumptions, worldviews, and perspectives to examine complex issues in sport. We position this paper as a starting point for advancing the field of sport marketing in meaningful and impactful ways by offering two research propositions, each accompanied by four actional recommendations. We employ a particular focus on the marketing campaigns that activate and promote corporate partnerships in sport to frame our two propositions, which discuss (1) consumer culture theory and (2) the circuit of culture as two important frameworks that begin to build bridges between sport marketing and critical social science.
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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 | 0.085 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.014 | 0.101 |
| Scholarly communication | 0.029 | 0.044 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".