Design and Implementation of a Genomics Field Trip Program Aimed at Secondary School Students
Bibliographic record
Abstract
With the rapid pace of advancements in biological research brought about by the application of computer science and information technology, we believe the time is right for introducing genomics and bioinformatics tools and concepts to secondary school students. Our approach has been to offer a full-day field trip in our research facility where secondary school students carry out experiments at the laboratory bench and on a laptop computer. This experience offers benefits for students, teachers, and field trip instructors. In delivering a wide variety of science outreach and education programs, we have learned that a number of factors contribute to designing a successful experience for secondary school students. First, it is important to engage students with authentic and fun activities that are linked to real-world applications and/or research questions. Second, connecting with a local high school teacher to pilot programs and linking to curricula taught in secondary schools will enrich the field trip experience. Whether or not programs are linked directly to local teachers, it is important to be flexible and build in mechanisms for collecting feedback in field trip programs. Finally, graduate students can be very powerful mentors for students and should be encouraged to share their enthusiasm for science and to talk about career paths. Our experiences suggest a real need for effective science outreach programs at the secondary school level and that genomics and bioinformatics are ideal areas to explore.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".