The stickiness of emotions in the field: complicating feminist methodologies
Bibliographic record
Abstract
This editorial theorizes the emotional entanglements that constitute spaces of fieldwork. Drawing on Sara Ahmed’s notion of sticky and circulating emotions, we develop the concept of emotional entanglements as a way to engage with the methodological implications of the emotional turn in geographic research. Beyond providing empirical evidence for research on emotional geographies, we argue that an attention to emotions in fieldwork has the potential to reinvigorate feminist practices of reflexivity and positionality. In addition, a critical engagement with emotions can offer novel epistemological techniques for studying the politics of knowledge production and the landscapes of power in which we, as researchers, are embedded. As the papers of this themed section demonstrate, analysis of emotional entanglements in research pose critical questions with regard to power relations, research ethics and the well- being of research participants and researchers alike. They also make visible how the power relations of sexism, racism, capitalism, nationalism and imperialism permeate and constitute the emotional spaces of the field. We use the notion of emotional entanglements as a way to situate the five articles of the themed section and to highlight the contribution of each paper to debates about the emotional field.
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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 | 0.069 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.088 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| 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".