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Record W7020350008

Learning from the Land: The Application of Archaeology and Land-Based Learning as an Experiential Learning Tool for Building Intercultural Competency

2023· article· en· W7020350008 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons (California Polytechnic State University) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Cultures and Socio-Education
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningIndigenousCognitive reframingIndigenous educationCurriculumValue (mathematics)Cultural learningTraditional knowledgeInclusion (mineral)Teaching method
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.264
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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