Bibliographic record
Abstract
The motivation for this analysis is twofold. First, it is my contention that the governance of the top tier of elite men’s professional football is today in an unprecedented state of disrepair, moral bankruptcy and public disrepute such that hitherto unimaginable transformations have become possible. Second, this chapter is interested in addressing what I take to be a significant lack in the growing body of critical writing about football. In his “Introduction” to Capital in the Twenty-First Century , French economist Thomas Piketty explains that the genesis of his sweeping analysis of inequality is a belief that historical debates among economists were a “dialogue of the deaf” 1 by virtue of the fact that “research on the distribution of wealth was for a long time based on a relatively limited set of firmly established facts together with a wide variety of purely theoretical speculations.” 2 He explains how his discipline has been historically blighted by an excess of deeply held theoretical views, all of which were based on very little or no hard evidence or concrete analysis grounded in data. More so than his conclusions or diagnoses, Piketty’s primary contribution to our thinking around inequality has been the creation of, and engagement with, massive data sets within which he looks for larger patterns and the theories to account for them. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".