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Record W3197651514 · doi:10.1002/ajhb.23673

There and back again: The biosocial dynamics of returning from the field

2021· article· en· W3197651514 on OpenAlexaff
Mallika S. Sarma, Theresa E. Gildner, Michaela Howells, Sheina Lew‐Levy, Benjamin C. Trumble, Agustín Fuentes

Bibliographic record

VenueAmerican Journal of Human Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsSimon Fraser University
FundersNational Institute on Deafness and Other Communication DisordersNational Institute on AgingKorea National Institute of Health
KeywordsBiosocial theoryField (mathematics)Dynamics (music)SociologyPsychologyPsychoanalysisMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.263
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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