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Record W7117361764 · doi:10.2478/slgr-2025-0021

From Crisis to Empowerment: Appraisal Patterns in Watt-Cloutier’s Ecobiography <i>The Right To Be Cold</i>

2025· article· en· W7117361764 on OpenAlexaboutno aff
Justyna Wawrzyniuk, Ewelina Feldman-Kołodziejuk

Bibliographic record

VenueStudies in Logic Grammar and Rhetoric · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive reframingSolidarityFraming (construction)Appraisal theoryNarrativeCritical appraisal

Abstract

fetched live from OpenAlex

Abstract This article applies the appraisal framework (Martin &amp; White, 2005; White, 2011) to Sheila Watt-Cloutier’s The Right to Be Cold (2018), analyzing how evaluative language shapes the ecological crisis discourse with a particular focus on Chapter Six, The Voices of the Hunters . The appraisal framework’s subsystems – Attitude, Engagement, and Graduation – reveal how Watt-Cloutier constructs meanings that interweave negative and positive appraisals. The framing of climate change as threatening, unstable, and destructive contrasts with positive a rmations of cultural heritage, Inuit expertise, and resilience. The analysis demonstrates how evaluative resources, including assessments of human capability, recognition of knowledge, and measurement of intensity, convert adversity into narratives of empowerment. By privileging the Positive Discourse Analysis approach, this study highlights how Watt-Cloutier mobilizes solidarity and ethical commitment to align readers emotionally and ethically with Inuit communities. Ultimately, the article demonstrates how the appraisal framework illuminates the evaluative strategies of ecobiography and environment-centered discourse, which can reframe ecological crises into a call for collective, life-sustaining action.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.348
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.041
GPT teacher head0.403
Teacher spread0.362 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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