Overview of IPR Practices for Publicly-funded Technologies
Bibliographic record
Abstract
The term technology transfer refers to a broad set of processes that cover the flows of know-how, experience, and equipment for mitigating and adapting to climate change amongst different stakeholders, including governments, the private sector, and financial institutions, environmental organizations, and research/education institutions. (Metz et al. 2000). Transfer encompasses diffusion of technologies and technology cooperation across and within countries, and forms one element of the overarching goal of the Climate Convention (UNFCCC) to stabilize greenhouse gas concentrations in the atmosphere. Governments devote varying amounts toward sponsoring or in some manner supporting a broad array of research activities pursuing a diverse set of outcomes ranging from medicine to energy and the environment. These activities can take place within government-owned facilities, private companies, or universities or some combination thereof. Such pursuits may result in the identification of a patentable technology or process, as well as copyrightable computer programs or other publications worthy of intellectual property rights (IPRs) protection. Although the precise arrangements vary from country to country, there is a high degree of commonality in the manner in which the property rights to these publicly-sponsored results are assigned. Except in the case of 'pure research' the property rights are assigned to one or more of the participants to the research process; government, university, private contractor, etc. For example, captured under the 'pure research' classification is genomic sequence data that is immediately shared with the public at large and to a significant extent climate data resulting from government-sponsored research is placed in the public domain. The results of this review are intended to inform the Expert Group on Technology Transfer as called for by 2005 program of work.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".