Experiential Learning and Archaeology: Reconciliation through Excavation
Bibliographic record
Abstract
The discipline of archaeology is uniquely positioned to allow for inclusion of culturally appropriate curricula to be incorporated into student learning objectives as mentioned in the 94 Calls to Action by the Truth and Reconciliation Commission of Canada (2015). In this paper the authors discuss the creation, implementation, and qualitative feedback of a community-directed and curriculum-based education program developed by graduate students that uses archaeology to mediate student learning and meet curriculum goals in both classroom and land-based environments. This experiential learning initiative involves graduate and undergraduate students, and students from a local Indigenous community and the surrounding area. Feedback from educators and student participants, both Indigenous and non-Indigenous found that the experience fostered a deeper understanding of longstanding histories of the land and increased cultural appreciation. The paper outlines program development, curriculum connections, community engagement, as well as educator and student feedback. This programming can be used as a framework, and the creation of local and place-based education initiatives is encouraged within other disciplines to facilitate pedagogy for reconciliation.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| 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".