Global Governance in All Its Discrete Forms: The Game, FIFA, and the Third World
Bibliographic record
Abstract
This paper uses the governance praxis of the Federation of International Football Associations [FIFA] to illustrate the impact of several intensive, discrete, and rarely studied global governance actors whose internal processes and procedures mirror the core concerns of Third World Approaches to International Law [TWAIL] scholars regarding the legitimation of a hegemonic category and the marginalization of Third World and subaltern interests. It is argued that FIFA has become an important international organization and global governance actor whose transnational rule-making characteristics should be studied in light of the incipient migration from “international law” to “global governance”. It will be shown that not only are FIFA’s rules impinging on sovereign imagination but that the tendencies of inequality, unfairness and domination afflicting the practices of traditional or state-centric international organizations are as prevalent in the procedures of such less-studied global governance actors regardless of the fact that their rule-making activities exert significant impact on governments, especially those in Africa and other parts of the Third World. More significantly, the essay looks at possible domestic, political, and socio-legal implications of discrete globalization of the kind exemplified by FIFA on Africa and the Third World and how important it is to integrate this concern into TWAIL scholarship going forward.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".