Science communication in transition: genomics hype, public engagement, education and commercialization pressures
Bibliographic record
Abstract
This essay reports on the final session of a 2-day workshop entitled 'Genetic Diversity and Science Communication', hosted by the CIHR Institute of Genetics in Toronto, April 2006. The first speaker, Timothy Caulfield, introduced the intersecting communities that promulgate a 'cycle of hype' of the timelines and expected outcomes of the Human Genome Project (HGP): scientists, the media and the public. Other actors also contribute to the overall hype, the social science and humanities communities, industry and politicians. There currently appears to be an abatement of the overblown rhetoric of the HGP. As pointed out by the second speaker, Sharon Kardia, there is broad recognition that most phenotypic traits, including disease susceptibility are multi-factorial. That said, George Davey-Smith reminded us that some direct genotype-phenotype associations may be useful for public health issues. The Mendelian randomization approach hopes to revitalize the discipline of epidemiology by strengthening causal influences about environmentally modifiable risk factors. A more realistic informational environment paves the way for greater public engagement in science policy. Two such initiatives were presented by Kardia and Jason Robert, and Peter Finegold emphasized that science education and professional development for science teachers are important components of later public engagement in science issues. However, pressures on public research institutions to commercialize and seek industry funding may have negative impacts in both encouraging scientists to inappropriately hype research and on diminishing public trust in the scientific enterprise. The latter may have a significant effect on public engagement processes, such as those proposed by Robert and Kardia.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".