Book Review: Innovation and Inequality: Emerging Technologies in an Unequal World by Susan Cozzens and Dhanaraj Thakur
Bibliographic record
Abstract
Cozzens, Susan, and Dhanaraj Thakur, eds. Innovation and Inequality: Emerging Technologies an Unequal World. Northampton: Edward Elgar Publishing, 2014. xii + 344 pages. Hardcover, $145.00. Public policy expert Susan Cozzens and political scientist Dhanaraj Thakur examine the relationship between emerging technologies and inequality this edited work, while reporting the results of comparative case studies tracing the costs and benefits of recombinant insulin, genetically modified corn, mobile phones, open-source software, and plant tissue culture on the economic well-being of eight nations across three continents. The editors were joined by a distinguished group of researchers, including consultants Isabel Bortagaray and Roland Brouwer, university professors Mario Paulo Falcao, Sonia D. Gatchair, and J. Adam Holbrook, postgraduate scholar Lisa A. Pace, UNESCO researcher Lidia Brito, and institute scholar Bernd Beckert. Using a broad definition of inequality, Cozzens and Thakur discover that the empirically-based case studies in fact reveal a more differentiated reality than theory would suggest (p. 8), thus calling into question previous conceptual literature. The book is divided into four parts. After identifying problems and concepts the Introduction, Cozzens and Thakur furnish overviews of the nations included the study, which represent the Americas (The United States, Canada, Jamaica, Costa Rica, Argentina), Europe (Germany), and Africa (Malta, Mozambique). Though all possess a democratic form of government, the countries differed size, national income levels, and science and technology resources. Part II of the text contains separate chapters on each of the emerging technologies. Regarding recombinant insulin--the only one of the new technologies that makes the difference between life and death--it was found to be widely distributed all of the nations studied despite constraints, albeit it was more accessible advanced than developing countries. In the discussion of genetically modified corn, the authors note the vast difference the regulatory approaches of the United States and Europe. Given that distinction, it is not surprising that researchers found uneven distribution the nations where such a crop is planted. Pertaining to mobile phones, the authors indicate that penetration rates exceeded 90 percent all of the nations studied except Canada and Mozambique. However, there are income disparities associated with access and regulation, and the production of phone components is still dominated by nations of the Global North. In analyzing open-source software, the researchers conclude that there are lower adoption rates developing countries due to affordability, skills, and enforcement. In the biotechnology area of plant tissue culture--the process of growing a new plant from the single cell of an older one--the authors assert that there are only a few highly skilled job opportunities associated with that technology and that public investment is needed to make benefits available more broadly. In Part III, the book's contributors apply the emerging technologies to the economic and cultural traits of the countries chosen and make policy recommendations. For example, Jamaican authorities are encouraged to seek external assistance order to promote diffusion of knowledge and skill. German officials are counseled to strengthen education computer science and programming as a way to improve shortcomings use of open-source software. To reduce inequality created by its public policies, Malta is encouraged to adopt a more transparent approach to its public sector decision-making practices. Finally, due to the fact that the United States needs world markets to be successful, it is suggested that they establish partnerships order to build a global human resource base for science and engineering. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".