Critical geographies and geography’s creative re/turn: poetics and practices for new disciplinary spaces
Bibliographic record
Abstract
We are two feminist geographers working as practitioners and researchers in creative geographies and the discipline’s creative re/turn. Human geographers interested in new representational and non-representational methods and methodologies are, as we explore in this article, increasingly turning to artistic and creative modes of expression, including (amongst others) literary and visual arts, in which we are both involved. For some time now, we have been curious about what we experience as a lack of expressly politicized critical interrogations of the discipline’s creative re/turn and a shortage of expressly critical and politicized creative outputs. In this article, then, we explore geography’s embrace of creative practices as research methods and as means of developing outputs but, more specifically, we ask about where and how decolonizing, feminist, anti-racist, and/or queer voices, practices, and theorizations might fit within the creative re/turn. Using two different creative geographic works (one a book of poetry, the other a curation project), we trouble what we conclude may be ongoing (perhaps unconsciously) masculinist, often White and colonial, perhaps overly heteronormative, modes of geographic inquiry and practice within geography’s creative re/turn. In this context, we reflexively consider our own creative practices as ones that may offer examples to open new critical spaces and modes of representation for creative geographers.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.024 | 0.205 |
| Scholarly communication | 0.037 | 0.030 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".