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Record W3092977076 · doi:10.3968/11821

Discussion of the Training Strategy for Aerospace Professionals in the New Era

2020· article· en· W3092977076 on OpenAlexvenueno aff
Xueai Yin

Bibliographic record

VenueHigher education of social science · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)AerospaceCreativityTraining (meteorology)SpecialtyBusinessEngineering managementMoment (physics)Engineering ethicsIndustrial organizationManagementPolitical scienceMarketingEngineeringEconomicsPsychologyGeographyLaw

Abstract

fetched live from OpenAlex

The competition between countries and industries in the new era mainly focuses on the cultivation of talents and the competition in creativity. This important period of new engineering development and the critical moment of educational transformation, the training of innovative, outstanding and characteristic talents has created new opportunities and challenges. It is therefore urgent to reshape, transform, optimize and perfect the talent cultivation strategy with professional characteristics, characteristics of the times and industry demands from cultivation schemes, cultivation modes and cultivation targets according to the development of aerospace specialty, industry and enterprise.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.099

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.319
Teacher spread0.284 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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