Settler-Author Allyship in Centering Indigenous Ecologies: Communal Will Through Collective Environmental Guilt in This Tender Land and Caleb's Crossing
Bibliographic record
Abstract
The January 2021 edition of PMLA housed an entire cluster on "Indigenous Literatures and the Anthropocene," in which at least four of the eight non-Indigenous contributors directly addressed and supported a call for learning from and collaborating with Indigenous voices. The unanimity of the discussion dissolves somewhat drastically when considering exactly how this should be done, leading Melanie Taylor to voice one of the framing questions of the cluster: "If it is increasingly clear that not all members of Anthropos are equal drivers of the Anthropocene, and that not all are uniformly compromised by its havoc, how can we begin to manufacture a communal will to redress it?" (Taylor 10). My thesis presents as a potential solution collective environmental guilt—collective guilt responding to the specifically ecological violence enacted by settler-societies. William Kent Krueger's This Tender Land and Geraldine Brook's Caleb's Crossing, two works of settler-authored historical fiction, utilize collective environmental guilt to manufacture a communal will in their popular readerships by demonstrating and assigning guilt to the settler-collectives of their protagonists before guiding readers to embrace and center Indigenous ecologies as a potential path to mitigating that guilt and promoting positive environmental change. As settler-authored works, the texts offer an alternative mode of engagement with Indigenous knowledges for an audience traditionally outside of scholarly discourse's reach in a way that models a path for ally authorship supporting Indigenous environmental movements.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.034 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".