Virtual School Counseling and Covid-19 as Seen Through an Organizational Learning Lens
Bibliographic record
Abstract
This article examines the shift to virtual school counselling through an organizational learning framework developed by Mary Crossan. This framework links the individual, group, and organization levels of an institution through the social and psychological processes of organizational learning, including intuiting, interpreting, integrating, and institutionalizing. The findings highlight four major challenges of virtual learning—technology, work-life balance, legal ramifications, and virtual counselling skills and abilities—that were partially or fully overcome with solutions that became institutionalized. RésuméCet article recourt à un cadre d’apprentissage organisationnel développé par MaryCrossan pour examiner la transition récente vers l’orientation scolaire virtuelle. Ce cadre relie les niveaux de l’individu, du groupe et de l’organisation d’une institution par le biais des processus sociaux et psychologiques de l’apprentissage organisationnel, y compris l’intuition, l’interprétation, l’intégration et l’institutionnalisation. Les résultats mettent en évidence quatre défis majeurs de l’apprentissage virtuel—la technologie, l’équilibre entre vie professionnelle et vie privée, les ramifications juridiques et les compétences et aptitudes en matière de conseil virtuel—qui ont été partiellement ou totalement surmontés grâce à des solutions qui par la suite ont pu être institutionnalisées. Keywords / Mots clés: virtual school counselling, organizational learning, virtual learning/ orientation scolaire virtuelle, apprentissage organisationnel, apprentissage virtuel
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".