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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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; both teacher heads agree on what is shown here.
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".