How Arnstein’s Ladder of Citizen Participation Can Enhance Community-Engaged Teaching and Learning
Bibliographic record
Abstract
Community-engaged teaching and learning (CETL) is an educational approach heralded as fostering student learning and social responsibility. However, prior research has noted the absence of consideration for the “community” component of this approach, including whether there is mutual benefit in the relationship between institutions and their community partners, and the extent to which the community has voice or power in the process and outcomes of CETL. To address this issue, we introduce a process-oriented framework based on theory that should help to advance best practices and scholarship in CETL: Arnstein’s (1969) Ladder of Citizen Participation. We then “test” this framework adapted for CETL by using it to assess examples of current practice of community participation in CETL, as evidenced in a purposeful cross-section of cases published in business and management education literature. Our findings suggest the Ladder provides meaningful differentiation among various forms of CETL and can offer effective guidance for achieving partnerships with mutual benefit, voice, and empowerment, and for identifying approaches that could limit community engagement in CETL. In this context, the framework can guide instructors to reflect on their practices and to explore what greater involvement of community partners in CETL may mean.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".