Elecciones y partidos en América Latina en el cambio del ciclo
Bibliographic record
Abstract
[EN] Between 2013 and the first quarter of 2017 every Latin American electoral democracy held legislative and, with the exception of Mexico, presidential elections. On the heels of a region-wide swing to the political left, many observers wondered what next might be in store. But it was almost a given that elections remained the only legitimate pathway power in the region during this period. Elections had become institutionalized in Latin America. Hence, the 2013-2017 election cycle granted as good an opportunity as any to take stock of party-system dynamics and representation in the region. This opportunity was not lost on Manuel Alcántara, Daniel Buquet, and María Laura Tagina, editors of Elecciones y partidos en América Latina en el cambio del ciclo (2018,\n\t\t\t\t 7).These accomplished scholars compiled twenty chapters spanning all eighteen electoral democracies in Latin America from 2013 to 2017. As stated in their introductory chapter, the book’s goal is to focus on “las transformaciones\n\t\t\t\t acaecidas en los respectivos sistemas de partidos a lo largo de las últimas décadas” and the extent to which, “marcaron un punto de inflexión… respecto de su desarrollo previo, o bien confirmaron tendencias preexistentes” (7). To undertake such a massive endeavor, the editors enlisted country\n\t\t\t\t experts from the ranks of doctoral students up to some of the most renowned political scientists in the region. The result is an extremely rich collection of essays that helps students of the region appreciate patterns of change within and between countries in the 2013-2017 cycle.\n\t\t\t\t My review analyzes three central aspects of the volume: (a) comparative approach; (b) theoretical puzzle; and (c) theoretical-conceptual innovation. Let us consider each in turn.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".