CROSSING OVER: GENOMICS IN THE PUBLIC ARENA
Bibliographic record
Abstract
Introduction / Edna Einsiedel -- Pt. 1. The financial pipeline -- 1. The challenge of creating, protecting and exploiting networked knowledge / Peter W.B. Phillips -- 2. From bank to bench to pharmacy shelf: biotechnology and the culture of finance / Usher Fleising and Chalres Mather -- Pt. 2. Policies and publics -- 3. Agricultural biotechnology in Europe at the crossroads / Hedge Torgersen -- 4. Angles of vision: stakeholders and human embryonic stem cell policy development / Robin Downey, Rose Gernansar, and Edna Einsiedel -- Pt. 3. From public perception to public participation: consumer choice and decision-making -- 5. Involving communities: a matter of trust and communication / Béatrice Godard -- 6. Canadian attitudes to genetically modified food / Michelle Veeman, Wiktor Adamowicz, Wuyang Hu, and Anne Hünnemeyer -- Pt. 4. Framing the gene: the media sphere -- 7. Media representations of genetic research / Tania M. Bubela and Timothy Caulfield -- 8. Lay people actively process messages about genetics / Celest M. Condit -- 9. Telling technological tales: the media and the evolution of biotechnology / Edna Einsiedel -- Pt. 5. Second thoughts: ethical issues -- 10. Ethical analysis of representation in the governance of biotechnology / Michael M. Burgess -- 11. The regulation of animal biotechnology: at the crossroads of law and ethics / Lyne Létourneau -- 12. Second thoughts on biobanks: the Icelandic experience / Gardar Árnason -- Pt. 6. Reflections beyond the here and now -- 13. In search of nanoscale economics: intellectual property and nano-mercantilism in the pre-assembler and assembler stages of nanotechnology / Michael D. Mehta and Linda Goldenberg -- 14. Inhuman, superhuman, or posthuman? Images of genetic futures / Jon Turney -- Pt. 7. Conclusions -- 15. The future of genomics / Peter W.B. Phillips and Edna Einsiedel -- Contributors -- Index
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".