Antropología de los medios de comunicación en Latinoamérica : comunidades, involucramientos y compromisos culturales en la era digital
Bibliographic record
Abstract
La antropología de los medios de comunicación está en pleno desarrollo gracias a la atención que le prodigan algunas de las más importantes casas de estudio en el mundo. Desarrollan ambiciosos proyectos etnográficos multisitiales, entre los que sobresalen los de la University College London (UCL); por ejemplo, el proyecto Anthropology of Smartphones and Smart Ageing, con etnografías multisitiales en 11 países, durante 16 meses, ambicioso proyecto que sucede a otro similar denominado Why We Post, ambos conducidos por Daniel Miller.1 En Australia, Annette Markham condujo, también durante 2020, el proyecto Massive and Microscopic: Making Sense of Covid-19, ejercicios testimoniales de autoetnografía desde el Digital Ethnography Research Centre del RMIT [Royal Melbourne Institute of Technology].2 Por otro lado, la Universidad de Toronto promueve Meet the Labs, encuentros de experiencias de laboratorios etnográficos: Stadtlabor for Multimodal Urban Anthropology de Berlín o el Kaleidos Center for Interdisciplinary Ethnography de Quito,3 entre otros.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".