Bibliographic record
Abstract
Desde sus inicios, Jangwa Pana ha publicado de manera constante contribuciones en el ámbito de la antropología urbana. Este campo, no solo explora las realidades sociomateriales en constante cambio y su heterogeneidad en las ciudades, sino también nos invita a reflexionar sobre los procesos que llevaron a los antropólogos a describir y comprender sus “propios” espacios (Gnecco y Gómez, 2005). En el proceso de construcción de este campo de estudio, la antropología se nutrió de otras áreas de las ciencias sociales que, gracias a la división intelectual del trabajo (Trouillot, 2003), se habían enfocado en estudiar el desarrollo de las ciudades, mientras que la antropología exploraba las vidas de habitantes “no occidentales” de selvas, montañas y desiertos. En este contexto nace este número, que presenta un conjunto de artículos agrupados en el dossier "Ciudades Intermedias de América Latina: dinámicas y perspectivas de investigación", coordinado por Claudia Duque de la Université Laval (Canadá), Hubert Mazurek del IRD-Aix Marseille Université (Francia) y Jorge Sánchez-Maldonado de la Corporación Universitaria del Meta (Colombia).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".