Engaging the optics community in the development of informative, accessible resources focusing on careers
Bibliographic record
Abstract
Young people often have biased and pre-conceived ideas about scientists and engineers that can dissuade them from considering a career in optics. This situation is compounded by the fact that existing resources on careers in optics are not suitable since they mostly focus on more general occupations such as a physicist and an electrical engineer. In addition, the linguistic register is not adapted for students, and many of these resources are only available to guidance counselors. To create appropriate resources that will inform high school students on different career opportunities in optics and photonics, we sought the collaboration of our local optics community. We selected seven specific occupations: entrepreneur in optics, university professor, teacher, technician, research and development engineer, sales representative and graduate student in optics. For each career, a list of daily tasks was created from the existing documentation by a guidance counselor and was validated by an expert working in the field of optics. Following a process of validation, we built surveys in which professionals were asked to select the tasks that best represented their occupation. The surveys were also used to gather other information such as level of education and advice for young people wishing to pursue careers in optics. Over 175 professionals answered the surveys. With these results, we created a leaflet and career cards that are available online and depict the activities of people working in optics and photonics. We hope that these resources will help counter the negative bias against scientific careers and inform teenagers and young adults on making career choices that are better suited to their preferences and aspirations.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".