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Record W2561896823 · doi:10.1080/23323256.2016.1235980

“Slow marriage,” “fast <i>bogadi</i> ”: change and continuity in marriage in Botswana

2016· article· en· W2561896823 on OpenAlexafffund
Jacqueline Solway

Bibliographic record

VenueAnthropology Southern Africa · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsTrent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsKinshipSociologyPhenomenonPersonhoodGender studiesIndividualismEmancipationPoliticsLawPolitical scienceAnthropology

Abstract

fetched live from OpenAlex

Classic work on Tswana marriage emphasises that it is a process of becoming, involving a series of rituals and prestations characterised by a long period of socially productive ambiguity in which the status of the union, the spouses, their children and their broader families remain uncertain. Marriage was a “total social phenomenon” entailing the intermingling of the economic, social and political spheres and continual gift circulation, thereby fostering dense social networks. In the twenty-first century, relatively few people marry, marriage is largely a middle class phenomenon, and people marry in civil ceremonies such that marriage is virtually instantaneous. Associated rituals occur over one or two days and bridewealth [bogadi] is usually given at the time of marriage. This article examines what such a time contraction in the rituals and prestations means and what it might suggest about marriage, the person and kinship. I propose two ideal types to capture this evolving process, “slow marriage” to depict marriages in the past and “fast bogadi’ to characterize contemporary marriages in which the rituals and gifts exchanged between marrying families occur over a brief time period. I draw on the concept of “possessive individualism” to help understand changing notions of personhood.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.998

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 studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.303
Teacher spread0.255 · 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 designObservational
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

Citations26
Published2016
Admission routes2
Has abstractyes

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