Bibliographic record
Abstract
The Internet is growing in popularity as a research site and is often framed as the next frontier in human subjects research. The opportunities the Internet provides for political organizing, making personal experiences more public, and creating spaces for a variety of voices makes it particularly relevant to feminist geographers and researchers such as ourselves. However, many qualitative researchers approach online research as though the Internet simply archives an abundance of data that is ‘there for the taking.’ Being trained in feminist research methods, we took issue with this approach, yet also encountered challenges when trying to apply feminist practices and ethical perspectives to online research environments. We explore these challenges through a collaborative reflection on our own independent online research experiences. Three themes emerge: (1) interpreting politics and visibility in online spaces, (2) researcher positionality across virtual and material study sites, and (3) subjectivity and power in online research ethics. Reflecting on these themes, we argue that the insights of feminist ethics and a feminist geographical lens are crucial for bringing much-needed reflexivity and reciprocity into online research. Simultaneously, online research opens up exciting new ways of conceptualizing central ideas within feminist research ethics, including politicization, positionality, and power.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".