‘Be a game changer and keep the ball rolling’: exploring linkages between football clubs, charitable foundations and doing good
Bibliographic record
Abstract
Purpose There has been limited research on why football clubs contribute to charity. This paper examines how football clubs and their charitable conduits report information when discussing their connectedness. In addition, it explores reasons why, and the extent to which, football clubs support altruism via such charitable vehicles. Design/methodology/approach Case studies of four major football teams (Manchester City/Manchester United in England and AC Milan/Inter Milan in Italy) are discussed, with formal reports of the clubs and their associated charitable conduits being analysed. Findings Boundaries between the clubs and their charitable conduits are frequently blurred. Evidence suggests that acknowledging the co-existence of different factors may help to understand what is reported by these organisations and address some of the caveats in terms of autonomy and probity of their activities and reporting practices. Research limitations/implications The research uses case studies of four major ‘powerhouses’ of the game and their associated charitable spinoffs. While this is innovative and novel, expanding the research to investigate more clubs and their charitable endeavours would allow greater generalisations. Practical implications The study provides material that can be used to reflect on the very topical subject of ‘sportswashing’. This has the potential to input to deliberations relating to the future governance of the game. Originality/value The paper explores relationships between businesses and charities/nonprofits in a sector so far little investigated from a charitable accountability perspective. It suggests that motives for engaging in charitable activity and highlighting such engagement may extend beyond normal altruism or warm-glow emotions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".