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Record W2959832078 · doi:10.1002/cl2.151

PROTOCOL: Vocational and business training to increase women's participation in higher skilled occupations in low‐ and middle‐income countries: protocol for a systematic review

2016· review· en· W2959832078 on OpenAlexaboutno aff
Marjorie Chinen, Thomas de Hoop, María Balarin, Lorena Alcázar

Bibliographic record

VenueCampbell Systematic Reviews · 2016
Typereview
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)Vocational educationLow and middle income countriesTraining (meteorology)BusinessPsychologyEconomicsEconomic growthDeveloping countryMedicineAlternative medicineGeography

Abstract

fetched live from OpenAlex

Th e Pr o b le mAlthough women's employment possibilities have improved with the rise of globalization, wom en in low-and m iddle-income countries tend to be overrepresented in informal labour markets, work in precarious conditions, receive lower salaries than m en, and have few opportunities for learning and advancem ent (Borges Månsson & Färnsveden, 20 12).Duflo (20 12) reports that "women are less likely to work, they earn less than men for similar work, and are m ore likely to be in poverty even when they work" (p.1052).Women often perform jobs that have low skill requirements and frequently work in occupations that are highly feminized, tend to be less socially valued, and pay lower wages (Aedo & Walker, 20 12; Altm an, 20 0 6; International Labour Organization [ILO], 20 15; ILO, 20 16).A recent ILO report has documented the limited opportunities for women in the labour market (ILO, 20 16).The report shows that women face higher unemployment and underem ploym ent than m en, are m ore often em ployed in the inform al labour m arket and in family enterprises, and are overrepresented in lower skill sectors.For exam ple, a greater proportion of women are employed in the services sector (61.5 per cent versus 42.6 per cent of m en), where wom en are particularly overrepresented in fem inized positions such as "clerical, services, and sales" and "elementary" occupations (ILO, 20 16).Moreover, although m en and wom en face equal rates of wage and salaried em ploym ent (around 52 per cent), m en are m ore likely to own their own business than wom en (3.7 per cent of men versus 1.4 per cent of wom en).Meanwhile, and while data on inform al sector employm ent is scant, wom en are 'believed to constitute most of the informal workforce in the developing world' (UNGEI 20 12).Work in the informal sector is characterised by low pay and low productivity (ILO, 20 16).Wom en working in inform al em ploym ent do not gain access to social protection, such as pensions, and this m ay contribute to the fact that 71.8 per cent of all em ployed wom en do not have any type of m aternity protection (ILO, 20 16).A range of factors contribute to the high proportion of unemployed and underem ployed wom en.These factors include cultural norms regarding the place of wom en in em ploym ent, the role of wom en in domestic and care work, and the lack of adequate job m arket opportunities.Women all over the world spend a disproportionate am ount of tim e doing domestic and care work.This tim e com m itm ent is even higher in low-and middle-incom e countries, where the division of domestic labor often follows traditional patterns and wom en assume most, if not all fam ily responsibilities (ILO, 20 0 9).Wom en m ay also have a preference for jobs that are compatible with their domestic responsibilities, such as parttim e and flexible jobs, both of which are scarce in low-and middle-incom e countries (ILO, 20 0 9).H owever, even when part-tim e and flexible job opportunities exist, allowing wom en to combine work and family responsibilities, there is evidence that these jobs do not constitute 'a path to decent work' (ILO, 20 0 5).This lack of opportunities contributes to the choice of wom en to remain self-em ployed in sm all-scale enterprises or in dom estic and care 18911803, 2016, 1, Downloaded from https://onlinelibrary.

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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)0.028
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.205
Threshold uncertainty score0.686

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.060
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0210.014
Bibliometrics0.0090.008
Science and technology studies0.0040.004
Scholarly communication0.0090.010
Open science0.0040.005
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.2050.023

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 head0.147
GPT teacher head0.430
Teacher spread0.283 · 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 designSystematic review
Domainnot available
GenreProtocol

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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