Bibliographic record
Abstract
In this article, I offer an analysis of the Canadian relationship with the land as a point of departure for educators to consider personal modes of resistance so that the curricular goals of communal responsibility for the land, and understanding within and across differences can begin and continue to flourish. Because of the reality of increasing encounters with difference in schools, teachers and students need space to examine their epistemological and ontological grounding: how they come to know who they are in the time and place of contemporary life. Relationships with images of the land are cogent aspects of this kind of deep, reflexive inquiry and pursuit of these connections involves critical visual literacy. Through a consideration of some forces that shape the development and maintenance of national identity in Canada, I examine the influences of images of wilderness on contemporary, collective life. In this light, I trace the historical evolution of the landscape work of the Group of Seven painters to the level of national icon. The hidden curriculum of “wilderness nation” is an influence that runs counter to the realities of many students’ learning experiences in Canadian schools. I discuss the ways that the signifier of wilderness maintains hegemonic, discriminatory practices within schools.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".