Social Technocracies: the emergence of a technocracy in the Ministry of Development and Social Inclusion
Bibliographic record
Abstract
This article analyzes the technocracy emergence in social sector, through the study of the Ministry of Development and Social Inclusion case, created in 2011 in response to one of the essential themes of Ollanta Humala’s electoral campaign: social inclusion. This paper aims to determine which factors led to the establishment of a technocracy linked to social policy in a country where it has been traditionally linked to political usage and patronage. There are three factors that explain the positioning of a technocracy in this ministry addressed throughout the article. On one hand, there was the presence of a consensus about the need for a technical management of this sector in the search of generating legitimacy and autonomy. On the other hand, it happened to be a favorable political environment characterized by a wide political support from the government. Finally, the wide discretion of the technical team in the design of MIDIS and during formation of the first ministerial body of bureaucrats allowed the emergence of a technocratic institution. To this end, this article describes development of the stages of the creation of the institution, design, approval and implementation and shows a corroboration of the technocratic profile of the initial top management team of this ministry.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".