From Crisis to Empowerment: Appraisal Patterns in Watt-Cloutier’s Ecobiography <i>The Right To Be Cold</i>
Bibliographic record
Abstract
Abstract This article applies the appraisal framework (Martin & 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.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".