Escolas de governo: um estudo comparativo
Bibliographic record
Abstract
The schools of government perform a central role to the public service of many countries. Despite being a disseminated and well-established phenomenon, the functions and even the concept of schools of government vary. Aiming to portray the functions and characteristics of schools of government around the world and offer a better understanding of them, this paper presents a comparative study of schools of government located across six continents. Based on purposive sampling, eight schools of government were selected to be part of this in depth study: École Nationale d'Administration, ENA – France; Canada School of Public Service, CSPS – Canada; Instituto Nacional de la Administración Pública, INAP – Argentina; Australia and New Zealand School of Government, ANZSOG – Australia and New Zealand; Civil Service College, CSC – Singapore; National School of Government, NSG – South Africa; Direcção Geral da Qualificação dos Trabalhadores em Funções Públicas, INA – Portugal; and Escuela Superior de Administración Pública, ESAP – Colombia. Data collection procedures included interviews and document analysis. Data were analyzed using content and comparative analysis. This study highlights some important dimensions of schools of government including the position within the government, funding, main activities, organizational structure and personnel. Other similarities (e.g. among members of Commonwealth) and possible common trends and innovation challenges are also discussed. Finally, we discuss the results comparing them to previous studies findings.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".