There and back again: The biosocial dynamics of returning from the field
Bibliographic record
Abstract
BACKGROUND: Leaving "home" to pursue fieldwork is a necessity but also a rite of passage for many biological anthropology/human biology scholars. Field-based scientists prepare for the potential changes to activity patterns, sleep schedules, social interactions, and more that come with going to the field. However, returning from extended fieldwork and the reverse-culture shock, discomforts, and mental shifts that are part of the return process can be jarring, sometimes traumatic experiences. A failure to acknowledge and address such experiences can compromise the health and wellbeing of those returning. AIMS: We argue for an engaged awareness of the difficult nature of returning from the field and offer suggestions for individuals and programs to better train and prepare PhD students pursuing fieldwork. MATERIALS & METHODS: Here, we offer personal stories of "coming back" and give professional insights on how to best ready students and scholars for returning from fieldwork. DISCUSSION/CONCLUSION: By bringing forward and normalizing the difficulty of the fieldwork-return process, we hope that this reflection acts as a tool for future scholars to prepare to come home as successfully and consciously as possible.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.039 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".