Mastercard, sa fondation et l’inclusion financière : une entreprise philanthropique ?
Bibliographic record
Abstract
En 2006, lors de son entrée en bourse, Mastercard a créé sa fondation en lui octroyant 10 % de ses actions et en lui donnant pour mission la lutte contre la pauvreté, par l’inclusion financière des populations exclues des circuits financiers formels en Afrique. Pour défendre cette cause sociale, la Fondation Mastercard, dotée d’un capital de 23 milliards de dollars en 2019, transfère dans le domaine philanthropique les discours et instruments du capitalisme financiarisé, tout en mobilisant des ong , des organisations internationales, des entreprises privées comme des universités. Sur la base d’une enquête empirique, cet article analyse les modalités de cette stratégie d’hybridation entre mondes militants et économiques et l’instrumentation croisée entre la firme et sa fondation. Il démontre comment la Fondation Mastercard vise à faire advenir la société dont a besoin la firme Mastercard : « A World Beyond Cash ».
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".