The World Bank and Inclusive Policymaking Practices [1980-2014]. A Theory of Change and Stability Through Collective Learning
Bibliographic record
Abstract
This dissertation presents a theory of change and stability of the World Bank based on collective learning processes and communities of practice. It is broadly divided into two parts. First, I aim to demonstrate how the Bank changed its policymaking practices between 1980 and 2014 in terms of [1] consultation with NGOs, [2] participation of citizens in design and implementation of projects, and [3] ownership by borrowing governments. This change, I argue, is due to three processes of collective learning: the interactions at the boundaries of communities of practice; and the constant contestation of practices. In the second part of the dissertation, I argue that in the end, this evolution of practice reinforced the Bank technocratic and neoliberal political rationality, thus promoting overall stability in its modus operandi. Building on French philosophers Pierre Dardot and Christian Laval, I seek to explain how change in an international organization (even if meaningful in many ways) can promote stability in its employees’ political rationality. Methodologically, this dissertation is based on a historical process-tracing of the period 1980-2014, using 41 first-hand interpretive interviews with World Bank employees and officials, 80 open source in-depth interviews with senior Bank managers, as well as an archive analysis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".