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Record W3094956686 · doi:10.1080/13600826.2020.1835833

Collective Learning at the Boundaries of Communities of Practice: Inclusive Policymaking at the World Bank

2020· article· en· W3094956686 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
Fundersnot available
KeywordsNegotiationScholarshipSociologyMeaning (existential)Boundary-workPolitical sciencePublic relationsEpistemologySocial scienceLaw

Abstract

fetched live from OpenAlex

This article explains the emergence of inclusive practices at the World Bank as a collective learning process between communities of practice. Contributing to the literature on practices and cognitive evolution in International Relations, this theory of learning goes beyond socialisation or meaning negotiation in communities in focusing on the translation of knowledge at the boundaries of communities of practice. This article also contributes to scholarship on international organisations in theorising communities and social processes that transcend formal boundaries. In brief, it develops three processes of change through collective learning (boundary encounters, brokerage, and the use of epistemic boundary objects) to understand the emergence of inclusive policymaking practices at the World Bank. Finally, it empirically explores how the Uganda Poverty Eradication Action Plan in 1997 participated in this collective learning. This research is based on 21 first-hand interviews, twenty publicly available interviews and extensive archival work.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.348
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it