‘The Datafication of Everything’: Toward a Sociology of Sport and Big Data
Bibliographic record
Abstract
This paper explores the articulations of sport and ‘Big Data’—an important though to date understudied topic. That we have arrived at an ‘Age of Big Data’ is an increasingly accepted premise: the proliferation of tracking technologies, combined with the desire to record/monitor human activity, has radically amplified the volume and variety of data in circulation, as well as the velocity at which data move. Herein, we take initial steps toward addressing the implications of Big Data for sport (and vice versa), first by historicizing the relationship between sport and quantification and second by charting its contemporary manifestations. We then present four overlapping postulates on sport in the Age of Big Data. These go toward both showing and questioning the logic of ‘progress’ said to lie at the core of sport’s nascent statistical turn. We conclude with reflections on how a robust sociology of sport and Big Data might be achieved.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.068 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| 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".