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Record W2051622570 · doi:10.1080/02680939.2010.520744

Educating for a high skills society? The landscape of federal employment, training and lifelong learning policy in Canada

2011· article· en· W2051622570 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

VenueJournal of Education Policy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLifelong learningGovernment (linguistics)ContradictionState (computer science)Public administrationPolitical scienceKnowledge economyPublic relationsPublic policyEconomic growthSociologyEconomicsPedagogyLaw

Abstract

fetched live from OpenAlex

Government reports and documents claim that building a knowledge economy and innovative society are key goals in Canada. In this paper, we draw on critical policy analysis to examine 10 Canadian federal government training and employment policies in relation to the government's espoused priorities of innovation and developing a high skills society and economy. Our findings highlight three areas of contradiction: a tension between high skills and low skills policy, a contradictory focus on the socially and economically excluded and included, and the paradox of both an active and passive federal government. Drawing on state theories such as inclusive liberalism and the social investment state, we argue that while a ‘highly skilled knowledge economy’ may form part of the overall skills discourse, these contradictions raise doubts that it is to become a reality in Canada in the near future.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.338
Teacher spread0.306 · 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