2011 URI Diversity Week Keynote Speaker Dr. Michio Kaku: Towards a Multicultural, Scientific, and Tolerant Future for the Planet
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Notice bibliographique
Résumé
Why is it important that the future be studied? Iranian-Canadian futurist Alireza Hejazi (2009) has suggested that the study of the future moves us “from a passive or fatalistic acceptance of what may happen to an active participation in creating preferred futures.” Why should the study of the future be democratized? German-Jewish futurist Robert Jungk (1987) observed, “Most developing nations seem to accept that their future lies in catching up with the present of the developed nations…This means that it is in the power of the rich nations to define and refine the future and to propagate their images…This is power – he (sic) who has insight into the future also controls some of the present. For that reason it is absolutely essential that futures research be internationalized as quickly as possible…The future belongs to all of us…” Renowned for his narrative vision of a utopian future in which science and technology have the capacity to eradicate disease and eliminate poverty, Japanese-American futurist Michio Kaku (2010), the keynote speaker for the 2010 URI Diversity Week, also cautions that we are globally a sub-Type 1 civilization on a scale in which Type 3 is highest. Our leanings toward warfare over natural resources and destruction of the planet could yet plunge us into self-destructive dystopia. Several of the 2010 URI Diversity Week workshops are reflections on exploring alternatives, choosing goals, and deciding on actions for the future.Diversity and the Knowledge EconomyDr. Kaku is an exemplary exponent of the mushrooming “knowledge economy”, which has become the primary template of the developed nations for transforming the creation of wealth, status, quality of life, and access to the work force. In the knowledge economy, the growth of value is driven by the individual and collective ability of knowledge professionals to produce, disseminate, and use knowledge. Science, technology, and globalization have been employed to enhance the power of the human brain to extend boundaries of human choice and control in knowledge-intensive fields, such as genomics, bio-informatics, robotic surgery, nanotechnology, biotechnology, design and engineering, software and web development, telecommunications, and neuroscience – most of these fields discussed during the 15th Annual URI Diversity Week. The relatively low levels of diversity among knowledge professionals in higher education and in industry worldwide help to explain the ever-widening gaps in income between rich and poor. Nationally, a recent Pew report found that the median wealth of white households is 20 times that of black households, and 18 times that of Latino households. Internationally, the world’s wealthiest 250 individuals enjoy wealth roughly equal to half the world’s population, and the richest 20% are 60 times richer per capita than the poorest. Australian chemistry professor Geoff Madigan (2005) raised these key questions: How do we insure that enough people have the skills to participate in the knowledge economy? How do we insure that the participants represent a broad swath of society?
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.
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 | 0,001 | 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 | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 découle