Masculinist Epistemologies and the Politics of Fieldwork in Latin Americanist Geography
Bibliographic record
Abstract
Given the importance of fieldwork in Latin Americanist geography, it is intriguing to note the absence of a dialogue about the politics of fieldwork within the subdiscipline. Drawing from feminist theories about the production of knowledge, this article suggests that the silence about fieldwork is rooted in masculinist epistemologies that predominate in Latin Americanist geography. After analyzing the epistemological and pedagogical implications of masculinism, I argue for increased attention to the nexus of power and knowledge and in particular, to how the researcher's geographic location, social status, race, and gender fundamentally shape the questions asked, the data collected, and the interpretation of the data. Dialogue about these issues in our teaching and writing not only will better prepare students for fieldwork, but also has the potential to foster research that subverts rather than reproduces power inequalities. *The thirty-seven individuals who responded to my survey, sent out in January 2001, made this article possible; my warmest appreciation goes out to each person who took the time to ponder and respond to my questions. An earlier version of this article was presented at the AAG Annual Meetings in New York in February 2001; as I had taken ill, I wish to thank Scott Prudham for delivering the paper on my behalf (a performance that is now legendary). I also thank Ines Mijares, Alison Mountz, Geraldine Pratt, Minelle Mahtani, and Scott Prudham for comments and encouragement. The five reviewers for this article provided additional insights that strengthened the article. However, I am responsible for the arguments presented here, as well as any and all errors.
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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.080 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".