Conducting Critical Analysis on International Communication Rights Standards: The Contributions of Graphical Knowledge Modeling
Bibliographic record
Abstract
Abstract Using the computerized application of Modeling using Object Types (MOT) theory, this article examines the normative dimension of official interpretations of a corpus of core “communication rights” (the right to freedom of opinion and expression, the right to privacy, the right to participate in cultural life, and the right to education) enshrined and protected by the International Covenants on Human Rights. This article proposes a methodological contribution whereby the computerized application of knowledge modeling theory promotes the analysis and popularization of international human rights standards. Research findings draw attention to significant conceptual deficiencies included as part of international human rights standards. These deficiencies undermine the applicability of these standards and their relative usefulness in the context of complex sociopolitical issues relating to communication. In addition, this article underscores the need for communication rights studies to further integrate contributions from the field of international human rights law research. It demonstrates that interdisciplinary dialogue can open up new research agendas for communication rights scholars and contribute to a renewed critical analysis of international human rights standards.
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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 | 0.153 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".