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Record W2499235693 · doi:10.1057/9780230369016_4

Networked Tools: How Does the OECD Do Its Governance Work?

2012· book-chapter· en· W2499235693 on OpenAlexaffabout
Leslie A. Pal

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsCarleton University
Fundersnot available
KeywordsHuman resourcesNegotiationGovernment (linguistics)Human resource managementPolitical scienceWork (physics)Corporate governancePublic administrationBusinessPublic relationsPublic sectorEngineeringFinance

Abstract

fetched live from OpenAlex

In 2010 the OECD published Brazil 2010: Federal Government, one in a series of reviews of human resource management in government. The 300-page document is a typical research product of the OECD — rather dry, well- researched, bursting with comparative insights from OECD members and how they manage their public sector human resources. But it is a result of a much more complicated and dynamic set of processes and interactions, of basic tools that the OECD uses to do its work. Brazil is not a member of the OECD, and so there were the initial negotiations over the parameters of the project and the eventual invitation by Brazil for the review. A decision was made to conduct the review in cooperation with the World Bank. Consultants were hired, and internal resources within GOV were mobilized — experts on pensions, job categorization, training and learning, reform implementation strategies, performance management, quantitative and qualitative data generation and analysis. There were study trips by OECD staff to build the initial data scan through meetings with relevant ministries and stakeholders — these included the Executive Secretariat in the Ministry of Planning; Secretariats of Human Resources, of Public Management, of International Relations; offices within at least three other ministries; the Ombudsman Office; the National School of Public Administration; public service unions; the Municipality of Rio de Janeiro; two institutes, and the University of São Paulo. And then in July 2009 came the signature instrument in the OECD toolkit — once the basic data had been assembled by OECD staff, five “high-level officials” (peers) from OECD member countries jetted in from Tokyo, Washington, Paris, Madrid and Ottawa to hear from stakeholders, ask questions, and provide subsequent guidance for the final drafting of the report. There was further drafting and discussion with Brazilian officials, and finally a submission to the PGC, where once again discussion ensued among members, speaking as representatives of member states. As we will see below, these types of reports and research comprise the ultimate “visible” product of the OECD’s work, but they are built on a foundation of networked interactions that - if we take the entire OECD and all its directorates and projects as a whole — take place daily in capital cities and ministries across the planet.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.031
GPT teacher head0.247
Teacher spread0.215 · 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
GenreOther

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

Citations0
Published2012
Admission routes2
Has abstractyes

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