Corporate Educational Philanthropy for Racialized Latin America: A Provocation for More Critical Studies
Bibliographic record
Abstract
ABSTRACT Since the 1990s, the corporatization of humanitarian aid and the expansion of private donors have filled the gap left by declining government investment in public education worldwide. As these corporate philanthropies take over work once done by governments, they gain spaces of power. This phenomenon has intensified with the global crisis of 2008. The paper asks what are the modalities of domination in corporate philanthropic educational interventions for racialized students in Latin America today, and how the critical literature interprets this phenomenon. It proposes a narrative review that reveals three main analytical trends. First, some studies examine how many corporate philanthropic interventions in education actually have a market colonizing motive. Second, other research focuses on the role of sponsored academic discourses, conventions, networks of influence, and social movements in shaping education policy reforms. Third, research shows that philanthropic efforts, despite their claims to “uplift” racialized students, often fail to disrupt the cycles of exclusion and subalternization they face. The paper concludes by highlighting the need for a broader critical framework for analyzing corporate philanthropic interventions in education, challenging the reductive and paternalistic representations of racialized students that often underlie these efforts, and understanding them within broader historical processes.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| 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".