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Record W2282708437 · doi:10.11575/prism/27096

New Mothers' Networks in the Canadian Context: A Combined Methods Investigation into the Characteristics, Function, and Dynamics of First Time Mothers' Social Networks

2013· dissertation· en· W2282708437 on OpenAlexaboutno aff
Carol Marie Cullingham

Bibliographic record

VenuePRISM (University of Calgary) · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Dynamics (music)Function (biology)Developmental psychologyData sciencePsychologyComputer scienceSociologyGeographyBiologyPedagogy

Abstract

fetched live from OpenAlex

In this combined methods study, semi-structured qualitative interview data and quantitative social network survey data were used to describe the characteristics, functions, and dynamics of new mothers’ social networks in the Canadian context. The social networks of the mothers who participated were largely composed of a core network of close family and friends who provided a range of social support. First time mothers’ networks also included network members who provided support specific to the context of new motherhood, such as daytime companionship during the regular work week, which they found through existing ties when possible. When not, new mothers often sought this companionship through acquaintances or new friends, particularly other new mothers. Comparison of structured social network data with semi-structured interview data led to recommendations for better eliciting and describing these context specific ties in new mothers’ networks, including the use of a multi-pronged, context aware approach.

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)0.004
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 score0.056
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.009
Science and technology studies0.0130.002
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.001
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 head0.008
GPT teacher head0.224
Teacher spread0.215 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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