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Record W4313034417 · doi:10.1177/1071181322661062

Empathy from Afar? Towards Empathy for Future Maritime Designers and Remote Operators

2022· article· en· W4313034417 on OpenAlexaff
Steven Mallam, Kjetil Nordby, Koen van de Merwe, E. Veitch, Salman Nazir, Brian Veitch

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2022
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsEmpathyRelevance (law)Context (archaeology)Knowledge managementValue (mathematics)Domain (mathematical analysis)Work (physics)Knowledge sharingPsychologyComputer scienceEngineeringSocial psychologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Having empathy and being able to empathize refer to the ability to understand, view or feel the experiences and perspectives of others. In a work context, the ability of different actors to empathize with others can have positive effects in the design, organization and operations of complex systems. This article explores the value of empathy within safety-critical work systems and discusses the role of empathy as an entry point for user-centred approaches. We use the maritime domain to illustrate why developing empathic skills and knowledge has relevance and added-value for (1) maritime design and designers, and (2) future remote maritime operations and operators of unmanned vessels. We detail our emerging approaches and methods for developing empathy as a tool to enhance interdisciplinary understanding and knowledge sharing with the overall goal of improving Human Factors utilization in applied work contexts.

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)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0070.009
Scholarly communication0.0090.008
Open science0.0010.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.021
GPT teacher head0.285
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2022
Admission routes1
Has abstractyes

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