Aplicación de la Convención sobre la Protección y Promoción de la Diversidad de las Expresiones Culturales en la era digital
Bibliographic record
Abstract
This report examines the impact of digital technologies on the way the diversity of cultural expressions is evolving. While digital technologies offer extraordinary possibilities for enriching the diversity of cultural expressions, they also increase the risk of certain cultures remaining on the sidelines. This study, however, rejects the idea of amending the 2005 Convention on the protection and promotion of the diversity of cultural expressions. The instrument implicitly conforms to the principle of technological neutrality. Allowing the Parties to take the particularities of the digital cultural ecosystem into account when they adopt policies and measures to protect and promote the diversity of cultural expressions. It proposes a number of topics for discussion with a view to adapting the implementation of the 2005 Convention to the particularities of the digital environment. This report calls on the Parties to react promptly to the new challenges posed by the reality of the digital world when implementing the 2005 Convention. It also invites the Parties to reject any form of compartmentalized discussions and to favour an open approach in order to take into account the way digital technologies are influencing the evolution of other legal instruments, notably trade agreements, whose e-commerce provisions may have an impact on the diversity of digital cultural expressions.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".