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Record W1603208635 · doi:10.21225/d5d60w

Lifelong Learning in the New Economy: A Great Leap Backwards

2001· article· en· W1603208635 on OpenAlexaffvenueabout
Jane Cruikshank

Bibliographic record

VenueCanadian Journal of University Continuing Education · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLifelong learningWageInvestment (military)Work (physics)Continuing educationLabour economicsLow wageBusinessEconomicsPolitical scienceEconomic growthPublic relationsEngineeringLaw

Abstract

fetched live from OpenAlex

This article looks at a 1994 Jobs Study report from the Organization for Economic Co-operation and Development (OECD) that presents a disturbing economic development strategy for use by its member countries. The report calls for the creation of two distinct streams of jobs: high-skill jobs that would have high-knowledge requirements; and low-skill, low-wage jobs that would absorb significant numbers of unemployed workers and for which the report advocates regressive ways to ensure workers are desperate enough to take the low-wage jobs. The concept of "lifelong learning" is central to the process of increasing the skills of those in the high-wage jobs, although it is seen solely as an investment in business and in the economies of OECD member countries. This article raises questions about the direction advocated in the report and explores some of the OECD strategies that have been adopted in Canada under the guise of "structural adjustment." The implications of this direction for university continuing education practice are examined and a social policy role for university continuing educators to play to address the issues of work and learning is discussed.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.268
Teacher spread0.250 · 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 designNot applicable
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

Citations14
Published2001
Admission routes3
Has abstractyes

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