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Record W2536780476

Creating a Blueprint for a Critical Social Pedagogy of Learning for Life and Work: Canadian Perspectives

2006· article· en· W2536780476 on OpenAlexaboutno aff
André P. Grace

Bibliographic record

VenueNew Prairie Press (Kansas State University) · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningBlueprintSocial learningWork (physics)Government (linguistics)Norm (philosophy)SociologyPedagogyPolitical sciencePublic relationsPsychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Abstract: In Canada, current federal learning-and-work policy is focused on individual learner-worker development using an iteration of lifelong learning as cyclical. Increasing numbers of disenfranchised young adults are resisting participation in such learning. In this regard, I consider the predicament of young adults in the Canadian province of Newfoundland and Labrador. To help us think about adequately addressing the dislocation they experience in life and work, I offer a Freire-informed vision of a critical social pedagogy of learning and work. Current Canadian federal learning-and-work policy aims to enhance the social as an effect of enhancing the economic. Such policy is focused on individual learner-worker development using an iteration of lifelong learning as cyclical. In this neoliberal milieu, cyclical lifelong learning has become not only a norm, but also a culture and an attitude. It is focused on assimilation, conditioning workers to get with the program, which is framed in neoliberal pragmatic terms. Still a current Canadian phenomenon indicates that increasing numbers of young adults are disengaging from participation in such learning that the federal government considers being a preventative measure. In this paper, I discuss young adults ’ reactions to what

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.026
GPT teacher head0.310
Teacher spread0.285 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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