Policy analysis in non-governmental organisations and the implementation of pro-diversity policies
Bibliographic record
Abstract
Introduction Unlike what happened in countries such as Canada and the US, Brazil did not experience the institutionalisation and the academic or professional maturation of traditional policy analysis. At the same time, today, it has to address contemporary dilemmas and discussions in this field. That is perhaps the very reason why the mix of policy analysis styles and methodological approaches is so particularly intense in this country. The purpose of this chapter in studying this mix is to contribute to a broader understanding of how policy analysis is being assimilated in Brazil. It does so – taking as its example non-governmental organisations (NGOs) that advocate for diversity – by showing how the encounter between the production and use of statistical evidence, arguments and advocacy contributed to the process of constituting actors who were an influence in deconstructing negative discourses about certain social segments, each characterised by a different identity attribute. These actors clamoured for different types of recognition and the institutionalisation of policies designed to reduce the inequalities anchored in these adverse discourses. The chapter also attempts to show how the practice of policy analysis can be linked to specific features of the contextual changes that simultaneously allow it to occur and are modelled by it. It also traces different pathways by which policy analysis is learnt even in the absence of the traditional formal structures generally involved in teaching it. The chapter draws on a diverse set of sources, both print (congress annals, open letters, personal correspondence, policy council minutes, research reports, and so on) and oral (interviews of members of various different groups and government representatives on rights councils). Struggles to secure respect for diversity in Brazil The feminist movement, the black movement, the gay rights movement and others have been crucial in spreading the diversity debate throughout Brazil. Especially since the 1970s, in the context of Brazil's re-democratisation, such movements led the struggle to defend and assert cultural differences and went on to demand greater recognition for the rights of segments with a history of social exclusion. Despite their numerous differences, these movements’ activities have been animated by at least one common element: the endeavour to dismantle adverse discourse on women and black and gay individuals.
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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 | 0.053 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".