Review of the initiatives in education and training tailored to industry needs over ten years of the SPIE Optics Education and Outreach Conference
Bibliographic record
Abstract
Initiatives in education and training tailored to industry needs have been a core part of the bi-annual SPIE Optics Education and Outreach conference, accounting for over 50% of papers since its inception in 2010. In this paper, the authors explore whether this conference has been useful to the readership by reviewing the contributions to this meeting for industry-oriented activities. Accounting for this scope, a bibliographic review of the literature of all five proceedings of OP301 is presented to describe the participants in terms of their affiliations and background, the proceedings’ taxonomy, and the metrics for downloads and citations. An integrative review will support this report to present lessons learned in six key areas of (1) technician training, (2) continuous education and training in industry, (3) in-company training and internships, (4) local and regional economic development through optics and photonics education and research, (5) progress on accreditation and certification, and (6) programs in innovation and entrepreneurship. The findings will be used to evaluate trends in formal and informal methods for industry-related programs and make recommendations for areas of potential focus for future meetings to continue serving wider segments of the community.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".