Trees Don't Talk: A Methodological Account of a Forest Sociologist in Mexico
Bibliographic record
Abstract
This paper examines personal experience as both a sociologist and forester collecting data in the southern state of Oaxaca, Mexico. It builds on writings where researchers have taken an introspective or auto/biographical approach to problematizing their own involvement. My findings illustrate that demographic and social features such as gender, nationality, and language can both hinder and privilege social science research. Moreover, this paper disputes the contention that expertise in a given specialty automatically makes for good field research. Depending upon the type of research and the questions being addressed, previous professional experience may actually hinder the building of rapport in certain cases. Genuine efforts to engage in local discourse can ultimately serve to improve fieldwork, and contribute to mutual understanding.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".