Book Review: Innovation and Inequality: Emerging Technologies in an Unequal World by Susan Cozzens and Dhanaraj Thakur
Notice bibliographique
Résumé
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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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 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 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 ».