Bibliographic record
Abstract
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 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 | 0.053 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.045 | 0.027 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".