Contesting the Curriculum: Counsellor Education in a Postmodern and Medicalising Era
Bibliographic record
Abstract
What is required to become a good counsellor these days? Most answers start with mention of ethical and responsive relational and conversational skills. However, beyond this initial answer, consensus would be hard to find. Look beyond graduate programmes in counsellor education, to what counsellors are expected to do in their everyday practice, and different notions of good counselling become evident. Counsellors are increasingly regulated as health practitioners whose services are funded by healthcare finances (Grohol 2013; Hansen 2007). With such medically oriented changes in funding and regulation have come professional identity challenges (Eriksen and Kress 2006). Counselling’s professional organisations have been renaming themselves as counselling and psychotherapy associations to position their members to receive health system funding (for example from the Canadian Counselling Association to the Canadian Counselling and Psychotherapy Association; De Cicco 2007). Such changes, beyond but still relevant to the universities, may be factors that are changing counsellor education. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".