Bibliographic record
Abstract
A call for cultural geographers to experiment with different ways of re-presencing their work has gained momentum in recent years (see DeLyser & Hawkins, 2014; Lorimer & Parr, 2014; Vannini, 2015). This climate of experimentation has seen a number of cultural geographers openly promote their interests in, and engagements with, the creative arts: some have explicitly developed practices in response to longer-standing geographical interests (e.g., Cresswell, 2013/2014; Gallagher, 2014; Gorman-Murray, 2014; Wylie, with Webster, 2014), while others have more established art practices that inform, and are informed by, their geographical work (e.g., Crouch, 2010; de Leeuw, 2012; Zebracki, n.d.). In this article, I explore the potential of poetry to animate accounts of geographical fieldwork via an intellectual engagement with the ideas and tenets of non-representational theory. I begin by outlining the history of ‘poetry as method’ in the social sciences and then acknowledge poetry’s status within phenomenology. From there, I consider what a post-structuralist account of geographical fieldwork might entail, drawing from Deleuzian philosophy. Then, using three conjoined poems of my own as a vehicle, I critically analyse the work that poems do as research as well as the ways in which they operate in literary terms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.153 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".