Indigenous Conceptual Cartographies and Landscape Pedagogy: Vibrant Modalities Across Semiotic Domains
Bibliographic record
Abstract
Abstract This chapter explores how aspects of the landscape can be incorporated in language teaching practices. Drawing on the area of research known as “linguistic landscape,” language teachers have recently begun to see the linguistic landscape as a pedagogical resource. Jaworski and Thurlow’s (2010) work broadens these ideas. They use the termsemiotic landscape, which is “any (public) space with visible inscription made through deliberate human intervention and meaning making” (p. 2). In addition, we link this approach to the notion ofindigenous conceptual cartographies, which we use to describe the multiple ways that indigenous teachers conceptualize language, landscape, and cosmology. This includes physical artifacts of cartographic representation such as maps, signs, and the landscape itself, as well as metaphorical cartographies such as ideas of the landscape, concepts of sustainability, and the relationships between language, landscape, and cosmology. We apply these concepts to one lesson that was organized as a narrated walking tour on the grounds of an indigenous community school, arguing that indigenous ways of learning in the landscape offer a rich experience that promotes not only language learning but also other learning that may help create a sustainable future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".