Bibliographic record
Abstract
A paper exploring the ways in which poetic narrative informs phenomenological experience through language, landscape, memory and belonging. My poetry aims to subvert existing hierarchies (of female/male, other/self, colonised/coloniser) with an emerging voice that accesses memories of a physical location other than the one currently occupied. For example, on a walk in Orkney (where I lived for a year as George Mackay Brown Writing Fellow), the narrator’s embodied experience is transformed from her current walk to memories of Canada. This realigns her subject position both temporally and spatially, giving voice to what was once unspoken. Memories infuse the physical details of experience by exploring questions of place and belonging through the naming of trees and rivers in British Columbia. Being immersed in and moving through the Scottish landscape – in the Highlands, Lowlands and Orkney – the ‘wildness’ of these spaces is evoked through the wind, light and sky. These constantly changing elements through which the poet walks act as a conduit for imagination, memory and transformation. The process of writing allows for transformation, which can be illustrated through use of the raven as ‘trickster’, with its ability to change form at will in Canadian indigenous (particularly Haida) stories. I will draw on the use of this ‘trickster’ figure in my poetry as a means of questioning one’s sense of self within these complex and ever-shifting relations. I will explore these and other details of the landscape – and the individual’s connection to them – through sensory perception and materiality. Colours, textures, smells, rain, hail, wind and sky (expanding with light, moving closer in darkness) create the ever-changing embodied experience of the individual, allowing poem and landscape to interact with and inform each other. The physical nature of the landscape (and potentially its ‘thingness’) gives life to the narrator, echoing the fragility and complexity of the individual and her natural environment. \nMy aim is to illustrate, through poetry, the ways in which language allows for transformation of the individual’s experience within a landscape, often subverting existing power structures in unexpected ways. \nThe above will draw from my experiences as a poet, including a year-long residency in Orkney as George Mackay Brown Writing Fellow and a recent three-day residency in Ullapool with Enough! Scotland, the latter looking at creative responses to the climate crisis. The presentation will also cite poems from my poetry collection, The Raven’s Song, illustrated and designed by artist CA Hiley and inspired by stories and myths of ravens and crows from Orkney, Shetland and Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".