Bibliographic record
Abstract
Bringing together literary ecocriticism and settler colonial studies, Tom Lynch's Outback and Out West sets out to describe the settler colonial imaginary, which the author defines as “a sort of settler unconscious that both crafts and constrains [settlers'] relationship to the lands they now inhabit” (p. 8). Through analysis of a diverse range of settler literary productions—some well known, some more obscure—Lynch argues that colonizers' narratives of the American West and the Australian outback emerge from common lineages of ideology, environmental practice, and settler desire for belonging in “new” places. Lynch's introduction sets the outback and the West against each other, noting their similarities (e.g., Indigenous dispossession, nuclear weapons, New Age spiritualities) and differences (e.g., soil fertility, Indigenous title, degree of urbanization). Drawing on well-known settler scholars such as Lorenzo Veracini—but not on the burgeoning body of critical Indigenous studies–centered work challenging many of the assumptions of settler colonial studies—Lynch discusses the ways the settler colonial imaginary “strives to make the settler process seem ordinary” (p. 24). Setting himself within the story (the use of “we” is common herein), Lynch notes the ways both the West and outback are forever-deferred never-never lands, defined as “not here” while also the grounds for very visceral conflicts over territory, meaning, and futurity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".