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Record W1982121814 · doi:10.1177/0022185609346185

Contrasting Management and Employment-Relations Strategies in European Airlines

2009· article· en· W1982121814 on OpenAlexaff
Greg J. Bamber, Jody Hoffer Gittell, Thomas A. Kochan, Andrew von Nordenflycht

Bibliographic record

VenueJournal of Industrial Relations · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsContext (archaeology)DeregulationCapitalismLiberalizationScope (computer science)Variety (cybernetics)BusinessIndustrial relationsMarket economyEconomicsPolitical sciencePoliticsManagement

Abstract

fetched live from OpenAlex

We discuss deregulation (liberalization) and some of the international institutions that influence the management of people in airlines. As a point of departure, we summarize contrasting models from successful ‘new entrant’ airlines: Ryanair and Southwest. We consider examples of various categories of airlines in different ‘ideal types’ of institutional context: liberal-market economies and coordinated-market economies. These are two varieties of advanced capitalism. The former include the USA, Britain, Ireland (and Australia). The latter include the Germanic and Scandinavian countries. We classify airlines according to which strategies dominate their efforts at cost reduction. Alongside these differences in strategies, we analyse differences in two aspects of employment-relations strategies. First, employers can focus on controlling employee behaviour or seeking their commitment to the goals of the airline. Second, employers can seek to avoid, accommodate or partner with unions. We show that, in terms of employment relations, the variety of capitalism context helps to influence employers’ strategies, but airlines (and other enterprises) still have some scope for exercising strategic choice, in spite of their institutional and regulatory context.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.313
Teacher spread0.266 · 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 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

Citations6
Published2009
Admission routes1
Has abstractyes

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