Knowledge Management and the Contextualisation of Intellectual Property Rights in Innovation Systems
Bibliographic record
Abstract
Intellectual property rights play a central role in biotechnology innovation. Patents, in particular, preoccupy research funding agencies, venture capitalists and governments, despite the fact that the value of patents is disputed and their impact continues to foster controversy. Perhaps more crucially to a fuller understanding of innovation, focus on instruments of intellectual property protection over-illuminates one stage of the flow of knowledge in innovation, leaving up- and down-stream phases in relative obscurity. Knowledge is an intangible asset, and is produced, tracked, managed, and accounted for in innovation systems. Yet what remains unclear, and this is problematic, are the respective roles of knowledge and intellectual property management, their relation, and the potential of a broadened perspective on knowledge flows in innovation. Participants at a Canada-U.K. workshop in Edinburgh examined the relationship between intellectual property rights and knowledge management by framing innovation in terms of knowledge management while attempting to bracket off the effects of patenting – the “Un-IP” approach. Eight critical issues arising at the heart of knowledge management and intellectual property rights were articulated, and general consensus emerged that, conceptually speaking, intellectual property rights needed to be subsumed under knowledge management as a particular class of intangible asset. At the same time, however, practical issues associated with patents continued to dominate the discussion, causing deviation away from the primary theme of the workshop, and highlighting the need to more fully explore eight emerging themes and contextualise the role of intellectual property rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.085 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".