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Record W4392596150 · doi:10.5194/egusphere-egu24-1090

Coming in from the cold: addressing the challenges experienced by women conducting remote polar fieldwork 

2024· preprint· en· W4392596150 on OpenAlexaffabout
Elaine Runge, Maria Dance, Rebecca J. Duncan, Marjolein Gevers, Eleanor Maedhbh Honan, Florina Roana Schalamon, Daniela Marianne Regina Walch

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsPolarTelecommunicationsComputer sciencePhysicsAstronomy

Abstract

fetched live from OpenAlex

Title: Coming in from the cold: addressing the challenges experienced by women conducting remote polar fieldwork Authors: 1. Runge, Elaine – Danish Hydrological Institute, Marine & Coastal Field Services, Agern Allé 5, Hørsholm, Denmark2. Dance, Maria - School of Geography and the Environment, University of Oxford, S Parks Rd, Oxford, UK3. Duncan, Rebecca Julianne - School of Life Sciences, University Technology Sydney, Broadway Rd Ultimo, Sydney, Australia and Department of Arctic Biology, University Centre in Svalbard, Longyearbyen, Norway4. Gevers, Marjolein - Institutes des dynamiques de la surface terrestre (IDYST), Université de Lausanne, Géopolis Mouline, 1015 Lausanne, Switzerland5. Honan, Eleanor Maedhbh - Department of Geography, Durham University, Durham, DH1 3LE, UK6. Schalamon, Florina Roana- Department of Geography and Regional Sciences, University of Graz, Heinrichstraße 36, 8010 Graz, Austria7. Walch, Daniela Marianne Regina - Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, 300 Allée des Ursulines, QC G5L 3A1, Rimouski, CanadaAbstract:Remote fieldwork is an important component of polar research within the physical and social sciences. Yet there is increasing recognition that the inherent logistical, physical, psychological, and interpersonal challenges of remote polar fieldwork are not felt equally across the polar research community, with minority groups often disproportionately affected. Although historically lacking diversity, the demographics of polar researchers have shifted and the way polar research is conducted has been changing in response. However, there are still barriers to equal participation. Removing these barriers would attract scientists from more diverse backgrounds and improve scientific outcomes. We explored the lived experiences of those who identify as women in polar fieldwork through a review of current literature and an anonymous survey, using existing networks to connect with women working in polar research. We synthesised and evaluated the literature and survey responses with regards to topics such as harassment, hygiene, inefficient communication, and gendered work expectations and responsibilities to form a holistic understanding of the key fieldwork challenges faced by women. The majority of survey respondents (80%, n=373) had encountered negative experiences during fieldwork, with the most common and impactful issues relating to field team dynamics and communication, sexism, rest, and weather. Many other issues including fieldwork preparation, work expectations, harassment, and personal space and privacy were also raised by respondents. From the recent developments and critical points of action that we identified in the literature and the survey, we propose strategies to remove barriers to participation and improve the experiences of women in polar fieldwork. These include strategies that are applicable on both an individual and organisational level. A diverse polar research community is imperative in order to address the challenges presented by current unprecedented climate change. Although we focussed on women’s experiences, through this study, we seek to advance the discourse on challenges faced by minorities in polar research.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0290.010
Scholarly communication0.0090.008
Open science0.0030.017
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0290.006

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 head0.224
GPT teacher head0.426
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations0
Published2024
Admission routes2
Has abstractyes

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