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Record W1892298149

Dual credit: Creating career and work possibilities for Canadian youth

2011· article· en· W1892298149 on OpenAlexvenueaboutno aff
Bonnie Watt‐Malcolm

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Dual (grammatical number)StakeholderWork (physics)Public relationsBusinessPolitical scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

In this article, I investigated the problems that educators are addressing with dual credit initiatives and consider tensions that have limited the seamless implementation of dual credit programming. Stakeholder representatives clearly identified common resources and support required to sustain their dual credit programs. Study participants noted the need for strong partnerships for dual credit initiatives, for government policies to allow for articulation of credits between secondary and post-secondary and, in particular, for the means to fund these programs. Findings from individual interviews and focus groups conducted in British Columbia and Ontario with individuals who had formal work-related involvement (e.g., industry associations, government, organized labour, high schools, school boards, colleges, and industry) in dual credit initiatives suggest that access to dual credit options provide secondary students valuable opportunities for future career and post- secondary education, giving support for policies to support stakeholder partnerships to advance the effectiveness of dual credit models. However, with partnerships, tensions exist because stakeholders compete to gain and maintain control of their institutional terri-tories and established standards. Questions arise – “who is going to pay?” and “who is going to benefit?” – that suggest concerns about student access and who is allowed to make use of the resources allotted for these initiatives.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.133
GPT teacher head0.316
Teacher spread0.182 · 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 designQualitative
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

Citations3
Published2011
Admission routes2
Has abstractyes

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