Bibliographic record
Abstract
The article that follows is the fifth in a series of articles on the nature of biotechnology and its cognate industries. The articles target specifically educational and training needs and trends in the global industry. The series is designed to facilitate the better understanding of the industry by the academic educational and research training sector, as well as to clarify the consequent educational, discovery research, applied research, and developmental research training needs of future employees. The articles will address aspects of biotechnology workforce creation and training in countries around the world. Some of the articles will be from governmental national science and technology agencies and officials that are charged with strategic biotechnology workforce development and with providing data to academic institutions so that they can conduct their own planning and resource acquisitional needs, as well as their consequent budgetary allocations for new or enhanced and industry-responsive educational and training programs. Importantly, the data from these various countries have great significance for industry clusters and regional academic institutions in other parts of the world. Regarding the cross-applicability of biotechnology workforce data and indicators between other regions and countries with a substantial biotechnology corporate presence, there will be many commonalities but also differences. The following article is from the Israeli Council for Higher Education and addresses emerging trends of academically trained new entrants in the labor market in Israel. There are interesting differences between Israel and the United States, specifically regarding the perceived central role of chemical engineering, biomedical engineering, and organic chemistry in the United States, especially in the small molecule bio/pharmaceutical, biomaterial, tissue engineering, and tissue replacement sectors. Future articles will be from other countries including Canada, New Zealand, Australia, the United Kingdom, and Germany. Although each country employs different approaches and mechanisms in collection and reporting of data, there are international efforts underway coordinated by the Organization for Economic Cooperation and Development (www.oecd.org) and its Working Party on Biotechnology in spearheading the collection of internationally comparable biotechnology workforce statistics via common analytical tools and benchmarking.
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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.213 | 0.088 |
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".