Bibliographic record
Abstract
This book offers an innovative framework and set of pedagogical pathways for deepening college student learning through critical engagement with place. Though the what and how of teaching and learning rightly take center stage in research of best practices, this book argues that the where of education deserves increased attention. Drawing from interviews and case studies with college and university educators in the United States and Canada, Learning on Location highlights pedagogies-in-action and identifies programmatic models for embedding location-based learning within specific courses, majors, curricula, and campus-wide initiatives. Chapters provide a mix of theoretical framing and practical application, with three key practices grounding the text: writing on location, walking on location, and engaging the civic on location. This resource is an invaluable guide for higher education faculty, leaders, and practitioners seeking to enhance student experience through attention to location, support identity-conscious student success, and use reflection and praxis to move toward more inclusive and equitable learning experiences. Supplemental resources—including example assignments, discussion questions for reading groups, and more—are available at www.centerforengagedlearning.org/books/learning-on-location.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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; both teacher heads agree on what is shown here.
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".