Bibliographic record
Abstract
On July 1, 2020, reforms to the Federal Copyright Act (LFDA, for its acronym in Spanish) entered into force in Mexico responding to the primarily economic requirements of the renewed free trade agreement with the United States and Canada, the USMCA. Facing these reforms, a group of Mexican and international associations and individuals raised their voices due to the numerous implications that they entailed for free speech, due judicial process, access to culture and education, technological sovereignty and their environmental impact, among others. In order to trace the deep reaching that the LFDA has today to the detriment of other rights and already established practices, from the Centro Cultural de España in Mexico City we proposed to inscribe these concerns and debate them on a broader sociocultural plane, starting from four conceptual nodes: 1) native knowledges; 2) open knowledge; 3) digital selfediting and rewriting; 4) hacktivisms. This book brings together contributions from Alberto López Cuenca, Anamhoo, David Cuartielles, Diana Macho Morales, Domingo M. Lechón, Eduardo Aguado-López, Gabriela Méndez Cota, Irene Soria, Leandro Rodríguez Medina, Marla Gutiérrez Gutiérrez, Mónica Nepote, Nika Zhenya, Renato Bermúdez Dini and Víctor Leonel Juan-Martínez.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.223 | 0.217 |
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".