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Record W1530792189

The Grand Old Duke of York; Present-Day and Future Canadian Geoscience Education and Labour Market Trends

2012· article· en· W1530792189 on OpenAlexaffvenueabout
Rob Raeside, Elisabeth Kosters

Bibliographic record

VenueGeoscience Canada · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsAcadia University
Fundersnot available
KeywordsPolitical scienceJob marketOrder (exchange)GeographyHumanitiesEconomyBusinessEconomicsArtEngineering
DOInot available

Abstract

fetched live from OpenAlex

The job market for Canadian earth scientists is significantly driven by the country’s strong resource industry. Enrolment in earth science degree programs is growing, but the demand from the job market is expected to outpace the projected supply from domestic programs. If the job market will indeed need as many workers as predicted, it will be necessary to change tactics for recruitment into Canadian university programs, in order to provide the market with the necessary workers. SOMMAIRE Le marche du travail canadien en sciences de la Terre est particulierement a l’importante industrie primaire du pays.  Le nombre des inscriptions a des programmes de diplomation en sciences de Terre est en croissance, mais on s’attend a ce que la demande du marche du travail depasse l'offre projetee des programmes nationaux de formation.  Si les besoins de l’emploi s’averent, il faudra qu’on change de tactique de recrutement dans les universites canadiennes pour les programmes geosciences, pour repondre a la demande.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.950
Threshold uncertainty score0.364

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0420.003

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.

Opus teacher head0.007
GPT teacher head0.184
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2012
Admission routes3
Has abstractyes

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