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Record W2015493856 · doi:10.1080/02601370701803617

Lifelong learning and the new economy: limitations of a market model

2008· article· en· W2015493856 on OpenAlexaffabout
Jane Cruikshank

Bibliographic record

VenueInternational Journal of Lifelong Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLifelong learningGrounded theoryPublic relationsPedagogyFocus groupWorkloadCareer PathwaysPerspective (graphical)Adult educationWork (physics)SociologyPolitical scienceQualitative researchMedical educationManagementSocial scienceMedicineEconomics

Abstract

fetched live from OpenAlex

What kind of workplace has the so‐called ‘new economy’ created? What problems are Canadian workers experiencing? How effective are Canada’s lifelong learning policies that focus on high skills development for global competitiveness? These questions were explored as part of a three year research program. During the 2003–2004 academic year, using a grounded theory research method and working from a critical perspective, I asked these questions to 11 union organizers and staff representatives. During the 2004–2005 academic year, I interviewed seven academic adult educators and asked for their analysis of the issues raised by the Year 1 research participants, and for feedback on my analysis of the Year 1 data. Three interrelated ‘work’ themes emerged from the interviews with union workers: increased workload, job insecurity, and loss of job satisfaction. Because of the workplace problems associated with the so‐called new economy, the research participants were highly critical of the current focus of lifelong learning. This article explores the work themes and their critique. Building on a worker perspective, this research questions the direction of current Canadian lifelong learning policies. It looks at the barriers adult educators encounter in trying to change these policies and suggests that, if change is to occur, Canadian adult educators must retake their place at the policy table.

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)0.002
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 score0.696
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.068
GPT teacher head0.372
Teacher spread0.305 · 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

Citations13
Published2008
Admission routes2
Has abstractyes

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