The stickiness of emotions in the field: complicating feminist methodologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it