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Record W2149548364 · doi:10.5539/ibr.v3n3p133

The Feasibility of Job Sharing as a Mechanism to Balance Work and Life of Female Entrepreneurs

2010· article· en· W2149548364 on OpenAlexvenueno aff
Aryan Gholipour, Mahdieh Bod, Mona Zehtabi, Ali Pirannejad, Samira Fakheri Kozekanan

Bibliographic record

VenueInternational Business Research · 2010
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersUniversity of Tehran
KeywordsNoticeBusinessBalance (ability)Job attitudeWork (physics)ProductivityMarketingPsychologyJob satisfactionJob performanceSocial psychologyEconomicsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

Balancing life and work is the most important challenge of entrepreneurs. Entrepreneurs seek a logical balance between their job and life and this issue is of great importance to Iranian entrepreneur women. Inability to handle the contrast between work and family is the main source of job stress and ends in job and personal dissatisfaction. To overcome these problems we have to look for solutions that not only meet the organization needs but also imposes less stress on entrepreneurs and thus results in improving their productivity.In this article we have studied female entrepreneurs situation and their share in labor market, as well as flexible methods of doing jobs especially job sharing method. We have done structured interviews with entrepreneurs of Azad University and their attitude to possibility of job sharing and its effect on female entrepreneurs at university has been expressed. After performing the qualitative methods, questionnaires were made and the attitude of female entrepreneurs towards job sharing was examined. The results of quantitative research show that they have a positive attitude towards job sharing but we have to notice that job sharing doesn’t lead in weakening the bargaining ability of female entrepreneurs.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 science0.0000.001
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 head0.185
GPT teacher head0.505
Teacher spread0.320 · 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

Citations22
Published2010
Admission routes1
Has abstractyes

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