MétaCan
Menu
Back to cohort
Record W1903292610 · doi:10.1787/5k9bb1vz5jhb-en

Skills for Competitiveness: Country Report for Canada

2012· paratext· en· W1903292610 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOECD local economic and employment development (LEED) working papers · 2012
Typeparatext
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRegional scienceOperations managementBusinessGeographyEconomics

Abstract

fetched live from OpenAlex

This study conducted an investigation of how regions can move to a higher-skill, higher value-added equilibrium in Canada, drawing on Ontario as a case study example. 1 Given the complexities of measuring such a shift this investigation examined the issue from different perspectives. It examined aggregate labour market data in order to map skills supply and demand at the level of employment insurance (EI) regions. This analysis is supplemented with an overview of institutions and policies that facilitate the shift to higher skills and higher value-added production in Ontario. Two geographical regions (Niagara and the Kitchener-Waterloo regions) and three industries within Ontario (food processing, hotels, food retailing) were examined in greater detail.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.279
Teacher spread0.257 · 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