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Record W4402512765 · doi:10.1080/14680777.2024.2400477

You’re doing it wrong: the governance of motherhood through mommy blogs

2024· article· en· W4402512765 on OpenAlexafffund
Erin Duebel, Shanon Phelan

Bibliographic record

VenueFeminist Media Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsDalhousie UniversityUniversity of Alberta
FundersUniversity of Alberta
KeywordsCorporate governanceInternet privacyPsychologySocial mediaSociologyPolitical scienceMedia studiesBusinessWorld Wide WebPublic relationsComputer science

Abstract

fetched live from OpenAlex

Mommy blog communities have been established by mothers who publish their stories online with the stated purpose to empower themselves and their peers by offering a seemingly unfiltered representation of motherhood. While this benefit has been felt by many readers, there are invisible governing discourses that have prevented such blogs from becoming authentic spaces of resistance to the hegemonic ideals of motherhood. To understand the governing forces placed upon mothers through “mommy blogs,” we explored the question: How is motherhood shaped through narratives (re)presented in mommy blogs? Critical discourse analysis, informed by a governmentality perspective, was used to analyze mommy blog entries to illuminate representations of power and knowledge within the blog entries. The findings suggest that a governmentality perspective is useful in understanding how mommy blogs enact power over mothers using mechanisms of governance through 1) sameness and imperfection, 2) perceived self-compassion, 3) advice and support, and 4) the scientification of motherhood. As more people turn to online communities for support with the experience of mothering, it is important to garner a critical understanding of how mommy blogs continue to influence contemporary experiences of motherhood.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.078
GPT teacher head0.366
Teacher spread0.289 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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