The IEEE oceanic engineering society (OES) and promotion of oceanic STEM sport competitions
Bibliographic record
Abstract
Advanced science, technology, engineering, and mathematics (STEM) are universally recognized as being pivotal to the advancement of humanity. STEM competitions promote the entry of youth into these fields in a very effective and at the same time very entertaining way. There are striking parallels between world class sport and STEM competitions that suggest that an in depth look at world class sport might lead to more effective worldwide promotion of STEM activity. Major world class sports induce great fervor, motivation, and interest in participants and observers. The fervor is strengthened by the ability to participate in chosen sports based on three characteristics or pillars, lifetime participation, international accessibility, and availability of a great variety of stimulating sporting possibilities. By designing STEM sport competitions with these three characteristics in mind, it is suggested in this paper that the fervor of participants can be maximized in their chosen field of activity. This paper will look in detail at the parallel relation between the characteristics of highly successful world class sport such as the Olympics and the fervor building potential possible in emulating those characteristics in STEM sports or competitions in the oceanic arena by an organization such as the IEEE OES.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".