Bibliographic record
Abstract
The Student Science and Technology Online Research Co-op Program provides mentors, including myself, an opportunity to further develop their mentoring skills, and to play a role in educating the next generation of scientists. This semester, I am mentoring my third student through the Co-op Program in the area of genetics and neuroscience. I have always loved teaching, especially teaching genetics, and have been thrilled to pass on my enthusiasm and knowledge to students by guiding them through their research projects. Mentors play a critical role. They can pass on their passion about the subject, promote curiosity and questioning, and offer the right balance between constructive criticism on projects, and words of encouragement. I am fortunate to have had (and continue to have) superb mentors throughout my science education and career who fit the above criteria, and I strive to be like them as a mentor in the Co-op Program. I cannot emphasize enough the importance of early exposure to research opportunities. My own educational research experiences had a profound effect on me choosing to pursue a career in research. For example, in my last year of undergraduate studies, I received offers to pursue three diverse pathways: optometry school, a Masters of Teaching Program, and a research-based graduate program. I had several early exposure to research; one of which, was an independent research project in Grade 12 Biology on the relationship between height and stride length in human adults compared to measurements from the fossil record of Homo sapiens’ predecessors. Another research experience was during my fourth-year of undergraduate studies when I participated in an undergraduate Research Opportunity Program. I investigated parent- of-origin expression bias, which assesses whether a gene inherited from one parent is expressed more often than the gene inherited from the other parent, in a set of genes related to a rare neurodevelopmental disorder. It was these positive experiences with scientific research that led me to a research-based graduate program and career in statistical genetic research as a post-doctoral research fellow at the University of Michigan. The articles in this issue demonstrate the skills, knowledge and passion gained by these Co-op students in specialized and interdisciplinary fields of research. The Student Science and Technology Online Research Co-op Program provides students with a wonderful opportunity to get exposed to the research and publication process, gain scientific literacy, as each field of science has its vocabulary to be mastered, and also hopefully instill a desire for lifelong learning.
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 | 0.016 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.201 | 0.211 |
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".