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Record W2928402776

Women, Work and the Changing Transport Industries

2006· preprint· en· W2928402776 on OpenAlexaboutno aff
Sarah Finke

Bibliographic record

VenueRePEc: Research Papers in Economics · 2006
Typepreprint
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceMultinational corporationContext (archaeology)GlobalizationBusinessWork (physics)Private sectorEuropean unionEconomic growthEconomic policyEconomicsMarket economyEngineeringFinanceGeography
DOInot available

Abstract

fetched live from OpenAlex

"The paper addresses some of the key issues facing women in the transport industries in recent years. In particular it examines the role of unions in pursuing equal job opportunities and working conditions in the world's transport industry. Using data available to the International Transport Federation (ITF) it looks at the changing face of the transport industry as the borders between different multinational companies disappear and the focus in transport employment changes from traditional operational occupations to logistics and the transport chain. In the context of the discussions that took place at the 2002 Congress in Vancouver, the Women's Committee of the ITF has promoted a two-tier policy that firstly, recognises that recruitment and organisation of women must be a priority for ITF unions, and secondly, demands that the ITF focus on how globalisation has led to changing employment structures that have specific gender effects. Women’s participation in the workforce has been increasing, but often as part-time and temporary workers and issues arising from these specific patterns need understanding and resolving. Union membership has also increased among women, but many of these new members are based in the public sector. A large proportion of the workers within the transport sector are in the private sector and their concerns may need to be addressed from a different perspective. In transport, the largest employment growth sectors have been areas where female employment is high, for example, air transport, the cruise industry and call centres. These are areas that can be called ’feminised’. However, union organisation here remains a challenge. These workplaces have low union density and high employee turnover. Criticism has also been levelled at trade unions for not keeping pace in terms of women's representation at senior levels - thus not encouraging female membership and not providing role models. Some unions, however, have been meeting these challenges and in several countries white-collar workers are now more likely to belong to a trade union than manual workers. It may be that additional measures are needed to face radical changes in the workforce. Many ITF unions have not yet addressed questions like organising informal workers, recruiting different groups of workers peripheral to their core membership, or organising in 'new' workplaces such as call centres. The challenges intrinsic in recruiting this different type of worker apply both to men and women, but the majority of the current target group are women. Other major challenges exist in making this shift. Strong cooperation with other global union federations and the International Confederation of Free Trade Unions (ICFTU) is necessary, for example, to ensure effective organising in call centre work. At the same time, the ITF's sectional structure, which clearly divides industry from industry, may in future make it practically difficult to work on multi-modal issues. Challenges to organising women internationally exist at the level of trade unions too, with the international message often failing to reach women workers."

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.014
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.034
GPT teacher head0.310
Teacher spread0.276 · 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 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

Citations0
Published2006
Admission routes1
Has abstractyes

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