Learning from the Land: The Application of Archaeology and Land-Based Learning as an Experiential Learning Tool for Building Intercultural Competency
Bibliographic record
Abstract
The written nature of Western society and oral basis of Indigenous society present a key difference in the way we approach the world (Duarte and Belarde-Lewis 2015; Kovach 2021; Scully 2012). Within an Indigenous ontology, there is an inseparable relationship between story and knowing and a holistic nature to this knowledge (Kovach 2021). Stories become a valuable tool for teaching and learning, which can also be used in other areas where value is placed on contextualized knowledge. Through the inclusion of Siksika (Blackfoot) Elders in our archaeology field school on the Siksika Nation, we attempt to present culturally appropriate curricula which increases student’s intercultural competency. Our study sought to evaluate our teaching pedagogy and to understand what value students attach to instructional methods which incorporate Indigenous teachers. Using the First Nations Holistic Lifelong Learning Model (Canadian Council for Learning 2007) as a guide, we examine data from student reflective journals to evaluate the cultural inclusivity of the curricula developed and its efficacy in increasing student’s intercultural competency. We demonstrate that the holistic curricula provided was highly valued, and that the land-based and immersive learning environment created allowed students to reframe their own previous biases and understandings which ultimately increased their intercultural competency.
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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 | 0.003 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".