The unexamined assumptions of intellectual property: adopting an evaluative approach to patenting biotechnological innovation.
Bibliographic record
Abstract
As intellectual property rights are increasingly the subject of national and international study, any deficiencies in our understanding of these rights and their place in society may have serious negative repercussions on policy formation. Different disciplines possess deep-rooted assumptions about the working of patents that have little grounding in either fact (a lack of empirical support) or theory (a failure to understand the nature of patent rights or the behaviour of market players). In addition, the prevailing fragmented approach to the analysis of intellectual property rights, whereby the various relevant disciplines work in isolation, leads to research results which can be incomplete or misleading. In an effort to address this deficiency in the development and implementation of intellectually property policy at both the national and international level, particularly in the field of biotechnology, the authors develop a transdisciplinary approach to the study of intellectual property rights. The goal of the authors’ work is to establish an effective and integrated conceptual framework, and the present article outlines a preliminary approach based on the results of the research conducted to date. While the primary unit of analysis is the design, use and implications of patent rights, this selection being based on the significance of patent rights in the field of biotechnology, the importance of other rights such as trade secrets and ordinary property rights in this area is acknowledged.
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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 | 0.110 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.089 |
| Scholarly communication | 0.028 | 0.034 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".