Philosophical analysis of the Recovery College learning model: characterization and connections to learning theories
Bibliographic record
Abstract
Introduction: The Recovery College (RC) model of learning is an innovative approach that originated in the UK in 2009 and has rapidly expanded, boasting over 130 locations in 22 countries by 2021. Grounded in the coproduction and recognition of various types of knowledge (clinical, experiential, theoretical), it fosters mental health, well-being, and social inclusion by bringing together diverse participants to learn collaboratively. However, despite its originality, few in-depth studies have examined its theoretical foundations, particularly its connection to social constructivism, which emphasizes collaborative learning and social interaction. A theoretical and philosophical analysis of this learning model would enhance our understanding of its mechanisms of action and enrich the pedagogical practices of RCs while considering adaptations for other contexts. Objectives: This study aims to define and characterize the Recovery College learning model and identify its connections with the key learning theories through a theoretical and philosophical analysis. Methodology: The study employs a hermeneutic philosophical approach consisting of six steps: 1. define and characterize the RC learning model, 2. identify, define, and describe the key learning theories, 3. select the perspectives and questions for philosophical analysis, 4. analyze the RC learning model through the chosen philosophical perspectives and questions, 5. identify the philosophical connections with the key learning theories, and 6. validate the analysis process. Results: The analysis identified five mechanisms of action, nine key principles of RC and four operations. RC integrates important concepts from social constructivism, cognitive constructivism, andragogy, and transformative learning, emphasizing collaborative, experiential, autonomous, and context-driven knowledge development. Philosophical analyses from epistemological, ethical, and political perspectives highlight RC's role in addressing epistemic justice, power relations, and inclusive learning spaces. Discussion: The Recovery College proposes an innovative approach that values the plurality of knowledge (clinical, experiential, theoretical) to redress epistemic injustices and rebalance relationships among different types of knowledge. Creating safe and egalitarian epistemic spaces supports inclusive learning aligned with principles of equity, diversity, and inclusion. Its ethico-political stance addresses systems of oppression (ableism, ageism, sanism) by bringing together diverse individuals in equality, thereby deconstructing stigma and prejudice. This approach, rooted in collaborative learning theories, transforms individuals and systems while enriching educational practices.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".