Mentorships and Related Programs Provide Mechanisms for Involving Students in the Science of Fats and Oils
Bibliographic record
Abstract
Worldwide shortages of scientists, engineers and technologists are being predicted in many scientific disciplines by organizations such as United Nations Education, Scientific and Cultural Organization (UNESCO; https://portal.unesco . org/en/ev.php). At the same time, science and technology have become the drivers of economic and social development for the global economy. Countries not able to compete in these attractive areas of growth in science and technology are more likely to face a lower standard of living and quality of life than their economic rivals. The pressure to prepare more young people to follow careers in science and technology, both generally and specifically in the field of oil chemistry, comes from at least two sources-the need to replace the retiring cadre of scientists and the general need for more scientists as science becomes more of a driver of economic development. Countries that have relied on immigration of scientists may especially feel the pressure to produce more scientists as more nations are able to offer attractive and rewarding careers. More scientists will find staying at home in their own country as rewarding as working elsewhere, most likely in a more industrialized nation. There are, of course, other variables related specifically to oil chemistry and science. Despite the importance of fats and oils, and in particular oilseeds, to the economy of Canada, there has been little effort in developing programs to teach the chemistry and technology of these components, especially at the post-secondary level of education. Even basic education in lipid chemistry and biochemistry has been noted as deficient by Canada’s Expert Committee on Fats, Oils and Other Lipids.1
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.184 | 0.063 |
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".