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Record W4390930328 · doi:10.46692/9781529210804.008

Religion, Recognition and Marriage Law

2023· other· en· W4390930328 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsLawPolitical science

Abstract

fetched live from OpenAlex

Karimah I came to the UK for marriage on a fiancée visa, and we got married on the same day in Islamic marriage ceremony. The marriage was arranged by my parents, especially my father. He trusted these people – they were from same community in Pakistan and my parents met them. I didn't meet the guy, I didn't see him, I didn't talk to him – just a few messages. So I came over here and I had a few things in the back of my mind, but I kept ignoring them. That was because my parents found out before the marriage that he has a daughter from his previous relationship. When I found out, I found that hard to accept, because I thought ‘I didn't do anything wrong so why do I deserve such a person?’ My husband talked to my dad all the time. And he said, ‘you know, I’m very guilty.’ So my dad thought that he's going to respect me even more because I’m willing to ignore this thing. My dad cried in front of me and said, ‘don't say no.’ He's suffered with depression before because of my sister's divorce. My family were also concerned because I had a childhood engagement with my cousin. He was super happy, but then he went to Canada and liked someone and he said, ‘I fell in love with someone.’ That time I was a really simple, shy person – I didn't talk to him and he said, ‘because you didn't talk to me, I went to someone else’. In our culture, if you get married and you’re good age, you’re respectful … if you’re not, they might be thinking there's something wrong in your personality, but this doesn't have to do anything with religion. So I came here, but the marriage didn't work from day one. I was just shocked. He left me at his parents’ house and he went to his own house. Sometimes, he would pick me up, and sometimes he would leave me in his parents’ house. He would say: ‘I’m the eldest son, you’re the eldest daughter-in-law – you have to do everything. You have to keep my parents happy. I have only one sister – she's my princess. I have only one daughter – she's my everything. My mother is my queen.’

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0190.001

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 head0.035
GPT teacher head0.300
Teacher spread0.265 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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