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Record W2724801066 · doi:10.1080/13688804.2017.1381551

‘Bicycle-Face’ and ‘Lawn Tennis’ Girls

2017· article· en· W2724801066 on OpenAlexaboutno aff
Hilary Marland

Bibliographic record

VenueMedia History · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
Fundersnot available
KeywordsVitalityViewpointsDiversity (politics)Face (sociological concept)EmblemQuarter (Canadian coin)Openness to experienceSociologyGender studiesPolitical scienceAestheticsPublic relationsMedia studiesHistorySocial scienceVisual artsPsychologyArtLaw

Abstract

fetched live from OpenAlex

In the final quarter of the nineteenth century, as periodical literature itself diversified and increased in volume, a growing amount of copy was devoted to the medical issues of the day, including debates about the limits of young women's energy and the impact of the extension of their activities in education, public life and sport on their health and vitality, and their future role as mothers. The article explores the ways in which doctors in particular utilized these outlets to convey their opinions and concerns, revealing a great diversity of viewpoints as well as the flexible editorial policies of many of these journals. Both male and a growing cohort of female doctors employed the platform of the periodical to popularize and make relevant medical ideas, while also building on, highlighting and creating broader cultural and gendered perspectives and emblems of girlhood.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.007
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.131
GPT teacher head0.256
Teacher spread0.125 · 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 designNot applicable
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

Citations5
Published2017
Admission routes1
Has abstractyes

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