Bibliographic record
Abstract
Our second issue for 2019 includes four research articles, a compelling and timely essay in our Perspectives section, and a book review. Fernando Filgueiras, Flávio Cireno, and Pedro Palotti’s contribution deals with the digital transformation process of public services in the Brazilian federal government. Antonio Alejo and Rebecka Villanueva Ulfgard’s article discusses how Mexico is moving from a state-centered foreign policy to network-oriented diplomacy. Daniel Zaga’s research calls for the implementation of a vertical industrial policy in Mexico, the goal of which would be to enhance local productive linkages and positive dynamic externalities. Finally, Francisco E. Campos provides a comparative assessment of the labor provisions in the United States–Mexico–Canada Agreement and the North American Free Trade Agreement, focusing mainly on the labor content of the rules of origin. In our Perspectives section, Arturo Sánchez assesses the first 10 months of the highly scrutinized administration of the Mexican president, Andrés López Obrador, and discusses some future concerns. We hope our readers will find this collection of studies fruitful and engaging. Isidro Morales is a researcher and professor in the School of Government and Public Transformation at the Tecnológico de Monterrey, and an external fellow of the Mexico–United States Center at Rice University’s Baker Institute.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.100 | 0.063 |
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".