Methodologically Becoming: Power, knowledge and team research
Bibliographic record
Abstract
This article explores the path of methodological and epistemological negotiation travelled by a team of four geographers conducting research among people with transnational connections between northern New Jersey and El Salvador. Having illustrated that all data are contextual, feminist scholars have explored the power relations in which data collection is embedded in order to situate knowledge. The relationship between the dynamics of research teams and the broader political struggles with which they engage, however, remains a blind spot within feminist field methods and writing strategies deployed to 'see accountably'. The authors argue that there is an undertheorised relationship between the politics of academic research projects and the broader political movements with which they engage that may serve as a fertile intersection for feminist research. They explore relationships between team, field, and institutions in the context of negotiating difference among team members and their aspirations for the project. The article contributes to discussions of power, knowledge construction, and the politics of conducting fieldwork as a team by relaying experiences both from the perspective of individuals on the team and the team as a whole. The authors depict their objectives, successes, failures, and research politics; all part of a process of methodological becoming.
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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 | 0.142 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.026 | 0.121 |
| Scholarly communication | 0.038 | 0.031 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".