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Record W2168098834 · doi:10.1177/0020852309365666

The history and future of nation-building? Building capacity for public results

2010· article· en· W2168098834 on OpenAlexaff
Jocelyne Bourgon

Bibliographic record

VenueInternational Review of Administrative Sciences · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsCentre for International Governance Innovation
Fundersnot available
KeywordsGovernment (linguistics)Face (sociological concept)Work (physics)Capacity buildingPublic relationsPublic administrationTheme (computing)Political scienceSociologyComputer scienceEngineeringSocial scienceLaw

Abstract

fetched live from OpenAlex

The goal of nation-building is to build the collective capacity to achieve public results and to pursue a shared vision of the future. This article, which is based on a theoretical vantage point and the author’s experience as a senior public official, explores the theme of collective capacity-building from the point of view of government. It describes how achieving collective results requires institutional and organizational capacities but, building on these foundations, governments must also develop greater capacity to anticipate, innovate and adapt in the face of increasingly complex public issues and unpredictable circumstances. Points for practitioners Building institutional capacity has been a focus of governments for many decades and, indeed, centuries. Building organizational capacity has been the centrepiece of reforms since the 1980s. But public organizations are not yet aligned with the complex problems they are expected to address. Addressing complexity and uncertainty will likely require practitioners to work with (i) a broader definition of public results, (ii) an expanded view of the role of government and of the range of possible relationships between government and citizens, and (iii) a more dynamic approach to public administration.

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.

How this classification was reachedexpand

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.187
GPT teacher head0.467
Teacher spread0.280 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2010
Admission routes1
Has abstractyes

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