From the Governmentality of Mobility to the Flaws of Income‐Distribution Data Used in Distributive Public Policies
Bibliographic record
Abstract
This first issue of Latin American Policy (LAMP) for 2017 addresses a myriad of topics encompassing theoretical perspectives, regional debates, and sectoral-specific policy analyses. Readers will find in this issue seven leading articles dealing with different subjects and perspectives. Marianne Marchand's article invites the reader to think of cross-border migration in terms of the governmentality of mobility and the creation of new “securitized” subjectivities. This idea contrasts greatly with Mexico's persistent “sovereignty” performance, even though Washington insists on considering the country as a “buffer” state. Gabriel Telleria explains the rise to power of the Sandinista National Liberation Front from the perspective of the Cuban tradition of “Vanguardism.” Meanwhile, Saidi Flores and Amado Villarreal elaborate a comparative analysis of the aerospace industry in Brazil, Canada, and Mexico, highlighting the shortcomings in Mexico. Mark Unger's contribution argues that the two pillars of environmental prosecution, the state and the law, are also its two main weaknesses. The three articles in the Policy Analysis section deal with the flaws and underestimations in the study of income inequality in Mexico. The three studies, submitted and discussed during a major workshop in Mexico City, shed new information on and offer methodologies for a more-accurate estimation of social inequalities in the country. At the end of this issue, readers will find our book-review section. We hope that the articles presented here will be useful for both scholars and policy practitioners. Isidro Morales is a researcher and professor in the School of Government and Public Transformation at the Tecnológico de Monterrey and an external fellow at the Mexico Center at Rice University's Baker Institute.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".