Landscape and reminiscence: towards an emotional geography of the University of New England
Bibliographic record
Abstract
"...no landscape - aesthetic, poetic, moral, material or surreal - has an objective appearance or significance independent of the beholder." It's 1984. Yes, literally. No Orwellian future time. Just that calendar year after 1983. It is winter and early morning. I lie asleep in a corner room of the three storey, grey concrete box that constitutes 'A' Block at St Albert's College. I am a 'fresher' on 'Bottom A'. Above me in, 'Middle A', some of my female friends in third year have already woken up. In fact, they are running about the corridor and knocking on everyone's door. "It's snowing! It's snowing!", they call. I quickly get up and, like everyone else, run first to my window and then to the courtyard outside to see snow falling. Flakes of ice swirl erratically between the gnarled and twisted branches of the wisteria that frames the courtyard. They fall to the ground and lie amongst the barren winter gardens. I add to the still growing group of bedraggled students gathered outside. We are witness to what will later be reported in the news as the biggest snowfall in Armidale for some 56 years.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".