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Record W4390056190 · doi:10.3389/fpsyg.2023.1210577

An introduction to the basic elements of the caste system of India

2023· article· en· W4390056190 on OpenAlexaff
Vina M. Goghari, Mavis Kusi

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Economic Development in India
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCasteOppressionColonialismIdentity (music)SociologyIntersectionalityAffirmative actionGender studiesPolitical scienceAnthropologyLawPolitics

Abstract

fetched live from OpenAlex

Oppression, systemic bias, and racism have unfortunately long been part of the human experience. This paper is a review of basic elements of the Indian caste system, understanding its impact on the daily lives of different caste members, the role of colonialism in perpetuating the caste system, the Indian reservation system for mitigating disadvantages created by the caste system, and how categorization and labels can affect individual identity. This paper then discusses the global relevance of the caste system and its impact on mental health and psychological functioning. In India, the caste system is a comprehensive, systematized, and institutionalized form of oppression of members of the lower castes, particularly the Dalits. Formalized during the British colonial period, the caste system brings together two related Indian concepts of varna and jāti to create four social orders and multiple subunits. Sitting outside the traditional four orders are the Dalits, who experience social, economic, and religious discrimination due to an inherited status related to traditionally polluting occupations. Since the caste system extends beyond India to other South Asian countries, as well as to communities around the world that are home to the Indian diaspora, the inequities created by the caste system are a global issue. India’s affirmative action system provides important insights to policy makers, as well as researchers in the social sciences for how to counteract the effects of systematized oppression. Collectively, this can aid in a better understanding of the effects of discrimination and oppression on identity, self-esteem, and mental health, and how we can develop more targeted policies and procedures in our own local contexts.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0110.003

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 head0.019
GPT teacher head0.311
Teacher spread0.293 · 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 designNot applicable
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

Citations29
Published2023
Admission routes1
Has abstractyes

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