Bibliographic record
Abstract
Part 1 Overview of Constructivism Chapter 1 Constructivism: What does it mean for career counselling? Wendy Patton (Queensland University of Technology, Australia) and Mary McMahon Chapter 2 Career Counselling Theory, Culture and Constructivism Mark Watson (University of Port Elizabeth, South Africa) Chapter 3 Usefulness and truthfulness: the limitations and benefits of constructivist approaches for career education, guidance and counselling Hazel L Reid (Canterbury Christ Church University College, England) Part 2 Constructivism, Culture and Career Counselling Chapter 4 The Systems Theory Framework: A conceptual and practical map for career counselling Mary McMahon and Wendy Patton (Queensland University of Technology, Australia) Chapter 5 Active Engagement and the Influence of Constructivism Norman E. Amundson (University of British Columbia, Canada) Chapter 6 The use of narratives in cross-cultural career counselling Kobus Maree and Jacobus Molepo (University of Pretoria, South Africa) Part 3 -- Constructivist Approaches to Career Counselling Chapter 7 Career narratives Elizabeth M. Grant and Joseph A. Johnston (University of Missouri, USA) Chapter 8 Using a solution-building approach in career counselling Judi Miller (University of Canterbury, New Zealand) Chapter 9 Sociodynamic counselling Timo Spangar, Finland Chapter 10 Working with storytellers: A metaphor for career counselling Mary McMahon Chapter 11 Creative approaches to career counselling Mary McMahon Chapter 12 Constructivist career assessment Mary McMahon and Wendy Patton (Queensland University of Technology, Australia) Part 4 -- Constructivist Career Assessment Chapter 13 Card Sorts: Constructivist Assessment Tools Polly Parker (The University of Auckland, New Zealand) Chapter 14 Constructivist tools on the Web Heidi Viljamaa (Careerstorm, Finland)
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.511 | 0.276 |
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".