Mapping the Wilderness: Toponymic Constructions of Cradle Mountain/Lake St Clair National Park, Tasmania, Australia
Bibliographic record
Abstract
This article traces the history of naming Cradle Mountain/Lake St. Clair (CM/LSC) National Park, in central western Tasmania, Australia, and, in doing so, will argue that toponyms constitute, rather than merely reflect, the landscape. The first official toponyms of the area were chosen by surveyors who visited the region in the early nineteenth century. These toponyms provide an insight into the European colonization of white settler nations, including the colonists’ desire to draw allegories between the newly discovered landscape and their European homeland. The surveyors were followed by local snarers, trappers, and farmers, and later by bushwalkers, and, through the toponyms given to CM/LSC, it is possible to consider the ways in which each of these groups has used this landscape. The article also examines other ways of knowing the landscape that are not necessarily reflected in the official toponyms. The construction of landscape through social practices such as naming is embedded within relationships of power, and this article will examine some of the ways in which the official toponyms may be contested. In particular, it will examine the differences between Aboriginal and non-Aboriginal ways of naming, and thus knowing, landscapes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".