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Record W2983736639 · doi:10.1111/awr.12174

Emotional Labor On and Off Water

2019· article· en· W2983736639 on OpenAlexaffabout
Sharon R. Roseman

Bibliographic record

VenueAnthropology of Work Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCrewArgument (complex analysis)Emotional laborFeelingCommercializationParticipant observationOrder (exchange)ReproductionSociologyBusinessPsychologySocial psychologyEngineeringMarketingAeronauticsSocial scienceFinance

Abstract

fetched live from OpenAlex

Abstract This article focuses on the emotional labor requirements of crew working within the intra‐provincial ferry system in Newfoundland and Labrador, on Canada's North Atlantic coast. The argument draws on fieldwork interviews with crew and passengers, participant observation on the most intensive daily maritime commuting route in the province, and documentary sources. It builds on the theoretical framework first laid out by Arlie Russell Hochschild in her 1983 book The Managed Heart: Commercialization of Human Feeling. As is the case of examples of emotional labor in other economic sectors, the crew working in this public transportation system regularly modulate their own emotional reactions in order to interact effectively with passengers and coworkers who are often contending with frequent delays and uncertainties in this ferry system, which can be considered an example of precarious aquamobility.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.022
GPT teacher head0.373
Teacher spread0.351 · 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
Published2019
Admission routes2
Has abstractyes

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