Exploring and Enhancing Experiential Teaching and Learning in Social Justice-Oriented Education
Bibliographic record
Abstract
Experiential education is a unique approach to teaching which can enhance student learning, engagement with material, and understanding of real-world situations. It particularly has potential to strengthen education in the social sciences, such as courses focused on topics related to social justice. This SaPP project aimed to explore experiential education in more detail in the literature and to map existing models of experiential education in disciplines related to social justice to identify ways to integrate experiential education more into SOCI 2170: Foundations in Social Justice, an undergraduate sociology course at Carleton University. The literature review conducted revealed service learning, problem-based learning, land-based learning, and place-based education as four key types of experiential education. The review of public course syllabi uncovered many samples of experiential education used in similar courses at other institutions. These literature findings and applicable real-world examples provide a strong foundation to improve experiential learning in SOCI 2170 for future years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".