Reframing the Picturesque in Contemporary Australian and Canadian Nature Writing
Bibliographic record
Abstract
This thesis explores aesthetic representation in Australian and Canadian nature writing from the turn of the twenty-first century to the present day. I analyse nine representative texts to explore the relationship between aesthetic representation of the so-called natural environment and the texts’ central themes, which I identify as (i) belonging (in place) (ii) digging (uncovering colonial history), (iii) walking (pilgrimage), and (iv) working (ecological rehabilitation). In connection with each theme, I examine how the environment is perceived, how notions of aesthetic value are constructed around it, and how aesthetic language¬¬ contributes to the narrative and argument of the text. In so doing, I seek insight from contemporary environmental aesthetics as developed by philosophers including Allan Carlson, Yuriko Saito, and Arnold Berleant. \n \nI argue that recent nature writing from both Australia and Canada shows an increasingly self-conscious engagement with the politics of representation that is often characterised by anxiety on the part of the narrator about representation and the possibility of the ‘truthful framing’ of place. This leads recent writers to enquire (albeit with different levels of success) into the discourses that drive beliefs about the natural environment. Some writers put pressure on popular modes of perception such as the picturesque by disrupting conventional representational styles, while others use those popular modes as the basis for a normative model of aesthetics and a spur to action. I suggest that one of the distinctive features of recent Australian and Canadian nature writing is its critical engagement with ways of seeing and describing nature that were developed during the colonial period, in particular in debates surrounding picturesque aesthetics, which in turn influenced travel and nature writing. In this way, much of contemporary Australian and Canadian nature writing can be seen as engaging, either explicitly or implicitly, in a critical project of reframing the picturesque. \n
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".