Diffusing financial practices in Latin American higher education
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine: how financial practices are diffused across countries and who are the carriers of diffusion; and to determine why the nature of adoption varies across countries and specific institutional fields and why certain practices are adopted in some settings but not in others. Design/methodology/approach In the macro portion of the study the authors document how World Bank loans in Latin America have encouraged the adoption of particular configurations of accounting and accountability practices. In the micro portion of the study, they analyze the cases of Guatemala and Mexico as a way of illustrating the ways in which the configuration of institutional players, capitals and habitus within these two sites have influenced the adoption of Bank recommended financial practices. Findings First, the analyses illustrate that the World Bank functions as an agent of diffusion via direct contact and through indirect modelling activities. Second, the analyses show that diffusion is not an automatic process – rather the predisposition of national governments, the embodied history of higher education and the distribution of capitals within the field influences whether financial reforms will be attempted. Third the analyses illustrate that, even when the introduction of new accounting and accountability mechanisms are attempted, other important field participants such as students can partially block the introduction of financial reforms. Originality/value The current study illustrates that international organizations such as the World Bank facilitate the diffusion of accounting and accountability practices but that local actors influence if, when and how accounting will be introduced and implemented.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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 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".