The history and future of nation-building? Building capacity for public results
Bibliographic record
Abstract
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.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".