Canadian Journal of Counse l l ing / Revue canadienne de counseling/1998, Vol. 32:1 75 Similarities and Differences Between Thesis Supervision and Counselling
Bibliographic record
Abstract
Thesis supervison can bring to light unresolved and new psychological issues for both super-visors and students. What makes thesis supervision especially difficult is that supervisors have no officiai mandate to deal with disruptive psychological processes. In fact, there are prohibitions against such interpersonal involvement. Those supervisors who move beyond the borders of traditional, rationally based, problem solving are engaging in a boodeg activity. The similarities and differences between the contexts of thesis supervision and counselling are discussed with a view to highlighting this problem and with particular reference to transference phenomena. Résumé L a direction de thèses peut r é v é l e r — p o u r les é tudiants et les directeurs—de nouvelles questions psychologiques non résolues. L'absence de mandat officiel pour r é soudre les pro-cessus psychologiques nuisibles peut rendre la tâche du directeur difficile. E n fait, i l est interdit au directeur d'intervenir d'une façon personnelle. Les directeurs dépassant le cadre tradition-nel et rationnel de la résolut ion de p rob lèmes le font à leurs risques et périls. Af in d'attirer
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.011 |
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".