Competencies for Providing Quality Careers Services: a look at Canadian Standards for Career Development
Bibliographic record
Abstract
espanolEn noviembre de 1996, en Canada, se empezaron a desarrollar unos Estandares y Directrices para los Servicios de Orientacion para el Desarrollo de la Carrera de ese pais. En este articulo se describe el contexto que dio lugar a esta iniciativa, las fases principales en su desarrollo, y se senalan los rasgos principales de estos estandares. Se destacan las ventajas y los puntos fuertes de esta iniciativa canadiense, haciendo enfasis en aquellos aspectos que pueden ser transferibles, con las oportunas adaptaciones, a otros paises EnglishIn November 1996, an initiative was begun to develop Standards and Guidelines for Career Development Service Providers in Canada.This paper provides the context that gave rise to this initiative, outlines the main stages in the developmental process, and highlights the main features of the standards. The paper concludes with a brief discussion of the strengths of the Canadian initiative, focusing on the features that are most transferable to other countries.-ABSTRACT In November 1996, an initiative was begun to develop Standards and GDevelopment Service Providers in Canada. This paper provides the context tuhiadte lgianvees rfiosre Ctoa rteheisr ithniet isattaivned,a rodust.l iTnhese tphaep emr acionn sctlaugdeess iwn itthhe a d bervieelfo dpimsceunstsailo np roofc ethsse, satnredn hgitghhs loigf htthse t hCea nmaadiina nf eiantiutiraetsiv oef, focusing on the features that are most transferable to other countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".