Bibliographic record
Abstract
In this article three interacting categories for the understanding of emergent digital activism are reviewed: tools that enable the participation, people as social agents, and contexts of social or political participation. Since in many of the most recent cases of digital activism the main actors have been young activists, the paper attempts to present how some of these new cohorts make use of technology mediated tools for social interaction, crowd engagement and participation, through a networked and apparently leaderless social and political activism. By analyzing the interaction between the existing tools and the generational characteristics of the social actors in particular situations we show the role that digital systems and new social media play in certain situations.Con el fin de comprender el activismo digital, en este artículo se exploran tres categorías que se entrelazan: las herramientas que facilitan los procesos de participación, las personas como agentes sociales que intervienen y los contextos de participación social y política. Dado que en los casos más recientes de activismo digital los principales actores pertenecen a cohortes juveniles, el artículo intenta presentar algunas ideas de cómo esos activistas hacen uso de las herramientas tecnológicas para la participación social, la ccoperación y la inteligencia de las multitudes, mediante un activismo en red aparentemente sin liderazgos notorios. Se hace el análisis de la interacción entre las herramientas tecnológicas y las características generacionales en tres casos particulares para ilustrar el papel que los sistemas digitales y los nuevo smedios sociales juegan en ciertas situaciones.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".