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Record W2163630241 · doi:10.25071/1916-4467.22103

The Hidden Curriculum of Wilderness: Images of Landscape in Canada

2009· article· en· W2163630241 on OpenAlexaffvenueabout
Patti Pente

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWildernessSociologyReflexivityCurriculumIdentity (music)HegemonyAestheticsSpace (punctuation)Social sciencePedagogyEpistemologyEnvironmental ethicsPolitical scienceLawPoliticsArtEcology

Abstract

fetched live from OpenAlex

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.239
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2009
Admission routes3
Has abstractyes

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