Bibliographic record
Abstract
As a White cis female researcher, I am often asked about my capacity to conduct meaningful, credible, and safe research with men. Questions often center on my experiences in men’s spaces, ability to understand or represent men’s experiences, and safety protocols to mitigate against looming threats of male-perpetrated violence. I am curious about how my gender continues to be a point of contention in my role as a qualitative researcher. In this meta-analysis and commentary article, I explore my experiences in relation to other female researchers who study men and who have published articles reflecting on gender norms in research practice. With examples taken from the contexts of fieldwork, qualitative interviews, and presentation of findings, this article illustrates the nuanced and often invisible power and gender dynamics that inform how methodological decisions are made, what is found or synthesized from qualitative data, and how problematic social norms are reinforced. I argue that, within the context of research about men and masculinities, researchers must be responsible for reflecting on and confronting gender norms as a part of their intersectional experiences of privilege and oppression. Specifically, researchers can use reflexive practice and field journaling to better understand how gender norms and uneven power dynamics are introduced to, co-constructed within, and generated from qualitative studies. These reflections and concerted efforts to confront broader social injustices imbedded in research practices are necessary for researchers to produce sound data and promote reciprocal research benefits. Without such efforts, researchers may reinforce the same structures of power and stereotypical gender norms that they aim to disrupt in their scholarship.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchScience and technology studies Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.423 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.033 | 0.111 |
| Scholarly communication | 0.035 | 0.036 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".