Bibliographic record
Abstract
With a scarcity of job options beyond the post-doctoral level, a growing number of researchers now see commercialization of their work as a viable career path. In fact, with the government and universities alike encouraging researchers to take their insight out of the lab and into the commercial sector, even those ensconced in faculty positions now regularly consider options beyond publication in academic journals. The benefits of learning how to navigate the commercialization process gives researchers new ways to build their reputations, expand their financial compensation options and even to improve their grant writing success. However, even for those researchers coming from an industry background, venturing into business can seem daunting. Can one become a world-class researcher and commercialize a concept at the same time? Answers to that question as well as tips for succeeding as a research focused entrepreneur are at the heart of this presentation. From understanding how to identify the value proposition your work can have for industry, to understanding how to create a dynamic founder's team, to raising capital, this presentation will prove that the development of a startup can be very compatible with scientific research. Featuring case studies and information about how to use 'futurecasting' to design research that will lead to commercialization, this presentation will leave the audience with actionable next steps they can use to begin (or enhance) the commercialization aspects of their careers.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".