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Record W7132939150

Unveiling the silence: Exploring memories of the 1947 partition through the voices of second generation Punjabi women

2007· dissertation· W7132939150 on OpenAlexaboutno aff
Mandeep Kaur Bhalru

Bibliographic record

VenueTSpace · 2007
Typedissertation
Language
FieldArts and Humanities
TopicSouth Asian Studies and Diaspora
Canadian institutionsnot available
Fundersnot available
KeywordsHonourPartition (number theory)Perspective (graphical)Resistance (ecology)Event (particle physics)Asian studies
DOInot available

Abstract

fetched live from OpenAlex

In 1947 India was partitioned into two countries, resulting in devastating violence nationwide. Within a span of months, twelve million people were displaced as they were forced to migrate to India or Pakistan according to religious identity. The trauma and communal violence that followed transpired on the bodies of women, as they became the symbols of family, community and national honour. Feminist scholars have recently explored the memories of this tragic event from the perspective of women; however, research exploring the intergenerational effects of Partition is sparse, particularly in the Diaspora. Similarly, literature that explores second-generation South Asian women's experiences growing up in Canada has yet to connect historical memories of the Partition with this generation. This research explores the experiences of seven, second-generation Punjabi women raised in Canada to families that were impacted by the Partition of India, delving into themes of honour and everyday resistance in their lives.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0350.025
Scholarly communication0.0100.004
Open science0.0030.010
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.097
GPT teacher head0.326
Teacher spread0.230 · 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 designQualitative
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
Published2007
Admission routes1
Has abstractyes

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