Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».