Knowledges in competition: Knowledge discourse at the World Bank during the Knowledge for Development era
Bibliographic record
Abstract
In 1996, World Bank President James D Wolfensohn announced the World Bank’s plan to evolve from a traditional financial institution into the world’s ‘Knowledge Bank’. This rhetorical shift within World Bank discourse marked the institution’s intention to rebrand itself as the world’s purveyor of knowledge about development. However, despite this seemingly progressive discourse, the Knowledge for Development era did not symbolize a real shift in World Bank policy or practice. Throughout the Knowledge for Development era, the World Bank held on to a narrow conceptualization of knowledge as capital to be leveraged for economic growth. As the World Bank’s knowledge activities continue to expand during the 21st century, the Knowledge for Development era serves as an opportune epoch to explore the World Bank’s expanding knowledge agenda. The article contends that it is possible to better understand the World Bank’s current knowledge agenda by showing how and why a particular type of knowledge became institutionalized within the World Bank during the 1990s. Using paradigm maintenance as a theoretical framework, the article illustrates how indigenous knowledge was widely acknowledged in World Bank research during the 1990s, yet ignored in World Bank policy and practice. More specifically, while a significant amount of World Bank research highlighted the importance of indigenous knowledge in education during this time period; such knowledge was never incorporated into the World Bank’s education strategy. Ultimately, this case study illustrates how the World Bank uses paradigm maintenance in order to maintain command and control over the ‘right’ type of knowledge for development.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.046 | 0.087 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".