Bibliographic record
Abstract
As a Western feminist supporting and researching gender equality in education in postcolonial contexts, I often wonder: Am I doing more harm than good? The privilege of my social location means that my efforts to support education in postcolonial contexts risk being patronizing, insulting, threatening, imperialist, and recolonizing. Yet neglecting and ignoring postcolonial contexts similarly reflects and reproduces a privileged position. I provide a tentative framework designed to address positionality, power, and privilege while creating an ethical research process for working in a postcolonial context. Beginning with an identification of positionality, the objectives of research, and guiding theoretical frameworks to situate the research in relation to the participants and context, I proceed to establish a methodology designed to minimize the negative effects of power and maximize participants’ empowerment. I position myself as a bricoleur, layering feminist standpoint theory and postcolonial theory, and propose the collaborative data collection and analysis techniques, with particular attention to ethical and cultural sensitivity, using a social constructivist approach to grounded theory. This article highlights the need for Western researchers to reflect upon the power dynamics of their research in postcolonial contexts and develop a strategy for conducting empowering research that prevents the misrepresentation and exploitation of participants. Observations from my doctoral thesis data collection provide examples of how these concepts were operationalized in practice as well as reflections on the disconnect between theorizing and conducting ethical research in postcolonial contexts.
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.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.042 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".