Bibliographic record
Abstract
Purpose This study examines the networks and strategy of Manchester City Football Club and City Football Group, central to the group's emergence as a global entertainment organisation with a network of franchises worldwide. Design/methodology/approach The study employs a case study design to examine both Manchester City Football Club and City Football Group (CFG)'s strategy. Drawing upon an extensive review of documentation pertaining to CFG's strategic vision and approach, a network analysis of the brand's constituent clubs, partners and state- and corporate-investors was conducted, providing a macro-level view of CFG's use of global franchising, media partnerships and commercial agreements to extend CFG and the City brand internationally. Findings The study's findings afford a unique insight into CFG's efforts to monetise and globalise through franchising, which provides insights into the convergence in sport of politics, entertainment and business. Namely, how the global strategy enacted by CFG and the Abu Dhabi government (its owner) has leveraged sporting properties successfully. In turn, it extends their geopolitical and economic networks and grows the parent City brand as a global entity. Research limitations/implications The study's findings afford a unique insight into CFG's efforts to monetise and globalise through franchising. Namely, the global strategy enacted by CFG and the Abu Dhabi royal family (its owner) has leveraged sporting properties successfully. In turn, it extends their geopolitical and economic networks and grows the parent City brand as a global entity. Originality/value The research represents an important step in examining the strategy of football club ownership and global club networks within sport. In this respect, the present research provides a new way to understand sport in a globalised, digitised and geopoliticised operating environment.
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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 | 0.007 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.548 | 0.465 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".