MétaCan
Menu
Back to cohort
Record W4316464479 · doi:10.52912/jsta.2022.2.4.231

A Review on the Space Human Resource Policy

2022· review· en· W4316464479 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Space Technology and Applications · 2022
Typereview
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsSpace (punctuation)Nature versus nurtureGovernment (linguistics)Human resourcesBusinessSpace policyResource (disambiguation)Operations researchEngineering managementComputer scienceEngineeringEconomicsManagementSociology

Abstract

fetched live from OpenAlex

A systematic strategy is required to train future-oriented space manpower. To this end, in this study, the discrepancy between demand and supply of space manpower was confirmed in terms of major, education, and job. It is necessary to systematize the level and range of capabilities required for space potential manpower and space technology/research manpower, and the space manpower training policy considering this is proposed as follows. First, it is necessary to strengthen the infrastructure for training space personnel. Second, a policy to train space potential manpower is needed. Third, policies are needed to nurture space technology and research personnel. These results can be modified and supplemented by considering the human, material and social resources of the government and businesses.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.028
GPT teacher head0.331
Teacher spread0.303 · 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