Indigenous Conceptual Cartographies and Landscape Pedagogy: Vibrant Modalities Across Semiotic Domains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 term semiotic 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 of indigenous 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.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it