2011 URI Diversity Week Keynote Speaker Dr. Michio Kaku: Towards a Multicultural, Scientific, and Tolerant Future for the Planet
Bibliographic record
Abstract
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?
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.078 | 0.026 |
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".