Bibliographic record
Abstract
Abstract FRAND (fair, reasonable and non-discriminatory) is a highly debated acronym that has been scrutinised, expounded and tested in scholarly works, courtrooms, and policy discussions. Initially employed as a remedy means and access tool in sector-specific and standards-setting contexts, FRAND has recently made its way into the emerging EU data governance regime. Given this new position, it is appropriate to examine FRAND critically as a universal access mechanism of the information law domain at large. Drawing on a narrative of FRAND in SEP licensing, this article reflects on the innate capacity of FRAND to act as a flexible governance instrument. Examining regulatory forces and patterns guiding the interpretation of FRAND, the article presents the interplay of self-governance, national and EU layers of regulation. As shown, the determination of FRAND is firmly grounded on institutional and procedural norms, both in a standardisation context and beyond. The analysis highlights the fact that such a regulatory approach has costs and opportunities. To harness the flexibility of FRAND as a universal information access tool, it is essential to gain more clarity as to the content and goals of FRAND-enabled transactions. It is also critical to ensure that the efficiency of such a regulatory approach does not come at the cost of compromising on the protection afforded by EU fundamental rights and freedoms.
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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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.083 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".