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Record W2806445382 · doi:10.3968/10176

Human Rights of Women and Challenges in Globalisation

2018· article· en· W2806445382 on OpenAlexvenueno aff
Rajkumar Singh, Chandra Prakash Singh

Bibliographic record

VenueCanadian social science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
Fundersnot available
KeywordsDignityHuman rightsGlobalizationCasteCreedSociologyContext (archaeology)PersonhoodPolitical scienceEconomic growthGender studiesEnvironmental ethicsLawSocial scienceHistoryEconomics

Abstract

fetched live from OpenAlex

In larger context human rights refer to those inalienable rights that a person is entitled to enjoy irrespective of his/her nationality, caste, creed, sex, colour, religion, income or any other socioeconomic divisive category. Human rights are those entitlements which an individual owns just because of his/her existence as a human being. The theoretical roots of human rights can be traced back to the history of ancient and medieval times in India as well as in the Western liberal and humanitarian philosophy. Both these roots have a considerable appeal of promoting human dignity. Human life is given a distinctive weight over other animals in most societies precisely because we are capable of cultivating the quality of our lives. Clearly both-human dignity and quality of life have negatively affected women’s human rights especially with the coming of globalization to a large number of countries. In the name of Structural Adjustment policies, publicly-funded health services education and child care have contributed to maternal mortality, made education unavailable for poorer children, particularly girls. The paper aims to examine the challenges of women in globalization in regards to their human rights in larger interest of women community as a whole with focus on developing countries or India like to give them a life with equality and human dignity.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score1.000

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 studies0.0010.002
Scholarly communication0.0000.000
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 head0.056
GPT teacher head0.308
Teacher spread0.252 · 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.

Study designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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