Bibliographic record
Abstract
Abstract Over the past few decades, scientific researchers have come under increasing pressure to commercialise their findings. This pressure and an increase in academic–industry connections have raised a number of concerns, including a potential loss of funding for basic science, the increased likelihood of researchers and universities finding themselves in (real or perceived) conflicts of interest and a potential loss of public support for research. In the human genetic research context, additional concerns have been raised including the legality of gene patents and their effect on innovation, the potentially premature marketing of genetic technologies and the readiness of health‐care systems, physicians and society for the increasing number and range of genetic testing options. While some of these concerns have proven less worrisome than others, the full extent of the impact of commercialisation of human genetic research is not yet known and there is much room for further research in this area. Key Concepts Commercialisation of scientific research can essentially be understood as the conversion of research findings into a commercial product or service (Downie and Herder, 2007) and can involve industry–university partnerships, patenting of research findings or spin‐off companies created as a result of research. Commercialisation of scientific research has grown significantly in recent years and is increasingly being prioritised by public funding bodies. As a result, the scientific community is under pressure to demonstrate the commercial value of their work. This is often referred to as ‘commercialisation pressure’. In the genetic research context, gene patenting has raised a lot of controversy in terms of its moral, ethical and legal validity. The legality of these patents has been debated around the world, and different jurisdictions appear to be coming to different conclusions on this issue. One concern that is frequently raised regarding gene patenting is its potential to negatively impact innovation by creating an ‘anticommons’ (Heller and Eisenberg, 1998). The ‘anticommons’ concern is essentially the idea that the growth of intellectual property protection will hinder researchers from accessing necessary research inputs, thereby slowing scientific progress and innovation. This concern has received a great deal of attention; however, recent research raises questions about the extent to which this concern has actually manifested. There has also been a lot of ‘hype’ surrounding genetic research, which has frequently been portrayed to the public in an overly optimistic manner in terms of its potential benefits and timelines in which these benefits are likely to materialise. This phenomenon is not necessarily the result of any intention to mislead the public but rather the product of many different pressures and incentive structures that scientists, research institutions and the media are operating under. Direct‐to‐consumer genetic testing has also raised concerns in terms of the marketing strategies employed by companies offering these tests and their ability to actually deliver useful health information to consumers. As a result, some regulatory bodies are taking a firm stand against companies marketing these tests, while others appear to be taking a more permissive stance.
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 | 0.051 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".