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The IEEE oceanic engineering society (OES) and promotion of oceanic STEM sport competitions

2015· article· en· W1507514146 on OpenAlexaff
James S. Collins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsParallelsClass (philosophy)HumanityPromotion (chess)Variety (cybernetics)World classEngineering ethicsEngineeringComputer sciencePolitical sciencePublic relationsArchitectural engineeringMechanical engineeringArtificial intelligenceLawPoliticsManufacturing engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.201
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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