How power relationships are involved in research methods
Bibliographic record
Abstract
With the rise of activism and activist research, this paper explores how power relationships are involved in traditional and emerging methods used in research on activism. This question matters as research methods have the potential to both improve the capacities of activist groups and enhance knowledge of agents involved: researcher and activist. The added value of the paper is that it presents a range of methods used in research on activism, including new methods that are relatively uncommon in planning research. The second contribution of this paper is that it is based on a power framework by Forester; it analyses how power is embedded in the use of a particular research method. The authors find extant differences between the methodologies when analyzed through this framework, especially in their potential to involve with activist communities. The authors encourage researchers to be braver in using activist research methods and to be aware of the underlying power discourses in their choices.
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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 | 0.570 | 0.642 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.015 | 0.079 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".