Book Review - Biotechnology Unglued: Science, Society and Social Cohesion
Bibliographic record
Abstract
In Biotechnology Unglued, Michael D. Mehta and an interdisciplinary team of experts explore “how advances in agricultural, medical, and forensic biotechnology may threaten the social cohesiveness of different kinds of communities and at different scales. The editor begins by discussing social cohesion and argue that a more cohesive society is better able to adjust to change, and can either minimize injustice or entrench the conditions that enable injustice. The authors explore how biotechnologies disrupt or “unglue” less cohesive communities, while socially cohesive communities stand to gain from new biotechnologies. The authors discuss biotechnologies in different contexts, such as the variegated impacts of genetically-modified agriculture on small- and large-scale farms, on communities in developing countries with low social cohesion, or the available state responses to genetically-modified foods in highly cohesive European societies in comparison to the American situation. The context and experience for the introduction and application of biotechnology strategies in health, in the criminal justice system and in academia are also discussed. The book is written in accessible language and it is appropriate for scholars or professionals in the the arts and sciences. It provides an engaging, thoughtful and practical analysis that breaks from traditional criticisms of biotechnology. However, it neglects crucial issues in biotechnology and social cohesion such as intellectual property rights, indigenous peoples and knowledge, disability, and how biotechnology policies and strategies are implicated in the controversial subject of gene therapy.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".