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Record W620718903

The politics of difference: non/recognition of the foreign credentials and prior work experience of immigrant professionals in Canada and Sweden

2006· article· en· W620718903 on OpenAlexaboutno aff
Shibao Guo, Per Andersson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningPoliticsImmigrationOrthodoxyPedagogyIdentity (music)SociologyExperiential knowledgePolitical scienceEpistemologyAestheticsGeography
DOInot available

Abstract

fetched live from OpenAlex

The recognition of prior learning (RPL) is an educational response to the need to widen participation in education and training for economic advancement and social inclusion. The social meanings of RPL have different configurations depending on historical, cultural, economic and political forces in different places. One constant is the reliance on the widely pervasive educational philosophies of experiential learning: constructivism and progressivism. This book challenges the orthodoxy of experiential learning and the particular readings of knowledge, pedagogy, learning, identity and power which it privileges. It does this by introducing different theoretical resources to RPL and drawing on experiences of RPL in the UK, the USA, Canada, South Africa, Australia, and Sweden. The book provides a range of re-conceptualisations of the relational terrain between adult experience and learning on the one hand, and specialist or academic knowledge on the other.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0380.020
Scholarly communication0.0120.003
Open science0.0020.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.327
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2006
Admission routes1
Has abstractyes

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