Towards a reframing of Eryri: how historic framings of landscape influence perceptions and expectations of a Welsh national park
Bibliographic record
Abstract
The landscape decision-making system in northwest Wales is insufficiently democratised and the main framings of Eryri (Snowdonia) are grounded in the perception and expectation that it is a sublime, distant and static landscape. Eryri, however, is changing. The landscape of the national park is already being impacted by climate change and the loss of biodiversity. Anticipated future change will also bring the need for further adaptation and transformation in land management. Historic framings of Eryri perpetuate ideologies and ambivalences that have, and could, continue to hamper the much-needed landscape change required to tackle today’s multiple crises. This article explores how past modes of representation, newly specialised industries and government legislation have perpetuated a limited understanding of Eryri. It links the eighteenth-century ‘top-down’, elitist rationalisation of the environment and the legacies of longing to find a ‘truly British’ landscape, with people’s current perceptions and expectations of the landscape. This article begins the journey of exposing the dominant ideologies of landscape, helping to define the underlying problem with the current prevailing framings of the landscape of the national park. It concludes by going beyond defining the problem and proposes an approach to actively reframe Eryri. To do this, it acknowledges the need to empower multiple voices, involving diverse forms of knowledge and incorporating new ways of representation within the landscape decision-making process.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".