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Record W4399515263 · doi:10.5821/mt.12845

Covid-19 and after: About human factors and welfare in shipping

2024· article· en· W4399515263 on OpenAlexfundno aff
Raphaël Baumler, Johan Hollander

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsnot available
FundersSveriges RegeringMinistry of Rural Affairs
KeywordsWelfareBusinessRepatriationCoronavirus disease 2019 (COVID-19)Work (physics)PopulationFinancial crisisHealth careEconomic growthEnvironmental healthMedicinePolitical scienceEconomicsEngineeringLaw

Abstract

fetched live from OpenAlex

The COVID-19 crisis locked sea workers on their ships for extended periods. They have been deprived of their right to repatriation or shore leave. Their employment agreements were extended, medical assistance failed, and financial challenges affected them. The crisis revealed the lack of care for seafarers, which may have mid-term and long-term consequences for this population. The study elaborates on 54 in-depth interviews and confirms challenges such as downgraded working conditions, including high work-related stress and few opportunities for recovery, prioritization of commercial interest over well-being, limited or no signs of improvements in seafarers' well-being, and insufficient care for seafarers. Additionally, the data indicates seafarers have been particularly resilient and adaptive during the crisis. However, the absence of care and respect combined with the insufficient recognition of their role after the crisis may seriously augment the intention to quit the occupation. Cooperation between the industry and authorities was deemed insufficient to protect seafarers' well-being and health globally. In conclusion, the COVID-19 crisis revealed the shipping industry's latent deficiencies in caring for seafarers. Interviewees agreed that the future of shipping depends on establishing a culture of care.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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.

Study designObservational
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
Published2024
Admission routes1
Has abstractyes

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