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Record W2276152172 · doi:10.7202/1078335ar

Cultural Constructions of Demographic Inquiry: Experiences of an Expatriate Researcher in Tunisia

2021· article· en· W2276152172 on OpenAlexaff
Roderic Beaujot

Bibliographic record

VenueCulture · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsWestern University
Fundersnot available
KeywordsExpatriateFertilityProcess (computing)SociologyCultural backgroundPolitical scienceResearch methodologyPopulationDemographyComputer science

Abstract

fetched live from OpenAlex

The average number of children per woman in Tunisia has declined from about seven in the mid 1960’s to about five in the early 1980’s, but the change has been slower over the last part of this period. In attempting to understand Tunisian society and its childbearing situation, we address questions of (1) sex roles, (2) conflicts over models of development and (3) tribal loyalties. The state has attempted to change sex roles, but there remain powerful traditional forces, especially from men, giving priority to women’s family roles. The conflict between Western and Islamic models of development implies that there is a wide element of ambivalence as people try to seek the advantages of both the small (Western) family and the large family corresponding to cultural traditions. For many, four children represent a type of compromise: “not too many, not too few”. Given the ways in which tribal loyalties become part of institutional dynamics, certain groups have come to perceive that Family Planning is acting not for them but for its own benefit. In the course of reconstructing these fertility dynamics, the author also stresses the experiences through which he learnt to situate the relevant issues.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.010
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.235
GPT teacher head0.480
Teacher spread0.246 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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