Bibliographic record
Abstract
Analysing the poetic ecology of the forest as a cultural landscape offers insight into ecocritical consciousness. This article compares Lithuanian national and Tasmanian colonial poetics, and examines post-colonial poetry linked to the Tasmanian conservation movement. Starting in nineteenth century Lithuania under Russian rule, this article examines The Forest of Anyksciai by Antanas Baranauskas in English translation. The poem, a national anthem to a cleared forest, reconstructs an entire ecosystem and imbues it with Lithuanian mythology. In this way, the poem re-inscribes Lithuanian forests as nationally significant, and inextricably linked to culture, sense of place and the struggle against Russian colonisation and imperialism. Nature becomes a nationally unifying symbol and forests in particular are represented as cultural landscapes. In far away Tasmania, the island state of Australia, a violent colonial past has also been unfolded in the setting of extensive forests. In nineteenth century Tasmania, however, the forest poetics were written by members of the colonising power, people who saw forests as hostile, dangerous places, and whose political agenda included the social legitimisation of the invasion of inhabited lands. Therefore, in many examples of Tasmanian colonial poetry, the representation of Nature as silent and empty of life deletes the ecological presence of forest. The silencing of the forest neatly accompanied the denial of any indigenous history of the land. This has had the effect of enacting the colonial doctrine of terra nullius and participating in a literature of indigenous “extinction.” More recently, a post-colonial re-awakening of the sound and breath of organic forest ecologies has occurred in poetry associated with the Tasmanian conservation movement. There has also been a deliberate re-inscription of indigenous history in post-colonial poetics that includes human interaction with Nature as part of an environmentally sustainable vision for the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".