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Record W2772232514 · doi:10.25071/1916-4467.40322

Curriculum Encounters Through Walking the City

2017· article· en· W2772232514 on OpenAlexaffvenueabout
Jennifer MacDonald

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNature versus nurtureSociologyCurriculumNarrativeFeelingReflexivityAestheticsSpace (punctuation)Environmental ethicsPsychologyPedagogySocial psychologySocial scienceAnthropologyArt

Abstract

fetched live from OpenAlex

In this paper, the author explores how the practice of walking the city may open curricular spaces to nurture a deep engagement and feelings of enchantment with the world. By disrupting the taken-for-granted sensibilities of our everyday urban lives and being open to the unexpected voices, bodies and more-than-human beings who co-exist in urban spaces, the author contends that when we slow down and become attuned to our surroundings, possibilities of transformation can emerge. In this interdisciplinary unfolding, the author first shares how walking allows us to experience time and space to accentuate our relations, engagements, and being in the world. Through narrative and photography, the author then reflects on encounters from recent walks through the city of Calgary, addressing notions of self-reflexivity, play and experience. Through these walking encounters, this paper reflects on considerations for embodying a curriculum to promote a modern ecological ethic.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0350.028
Scholarly communication0.0100.007
Open science0.0020.019
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0100.001

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 head0.060
GPT teacher head0.368
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2017
Admission routes3
Has abstractyes

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