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Record W1812824803 · doi:10.29173/cmplct20402

Winter Counts as Transformative Inquiry: The Role of Creative Imagery as an Expression of Adaptive Change

2013· article· en· W1812824803 on OpenAlexfundvenueno aff
Nick Stanger, Michele Tanaka, Vanessa V. Tse, Lisa J. Starr

Bibliographic record

VenueComplicity An International Journal of Complexity and Education · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsTransformative learningContext (archaeology)Expression (computer science)NegotiationSociologySet (abstract data type)Process (computing)EpistemologyTerrainPsychologySocial sciencePedagogyComputer scienceEcologyGeography

Abstract

fetched live from OpenAlex

Pre-service teachers face a complex educational context and Transformative Inquiry is a useful approach for negotiating this terrain. We interpret the movement of students via the adaptive cycle put forth in panarchy theory as they engage in the inquiry process through ‘winter counts’, a Plains First Nation tradition, as expressions of their understanding. These image-based expressions demonstrate the emotional, mental, spiritual and physical movement students have made within their inquiry. Panarchy theory moves beyond interpreting systems using simplistic equilibrium models and acknowledging the more complex and dynamic set of equilibria that describes transformation in ecological, social, and economic systems and considers the multiple complexities of systems thinking while providing insight into how change occurs as a constantly adaptive cycle process. Used sparingly within social sciences until recently, we argue it as particularly relevant for seeing Transformative Inquiry through Indigenist and interconnected lenses.

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)0.005
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.055
Scholarly communication0.0100.012
Open science0.0010.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.322
Teacher spread0.278 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueComplicity An International Journal of Complexity and EducationSame topicEnvironmental Education and SustainabilityFrench-language works237,207