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Record W4392056661 · doi:10.51644/9780889207301

“I Want to Join Your Club”

2006· book· en· W4392056661 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsJoin (topology)ClubComputer scienceMathematicsGeologyCombinatorics

Abstract

fetched live from OpenAlex

“I am a girl, 13 years old, and a proper broncho buster. I can cook and do housework, but I just love to ride.” In letters written to the children’s pages of newspapers, we hear the clear and authentic voices of real children who lived in rural Canada and Newfoundland between 1900 and 1920. Children tell us about their families, their schools, jobs and communities and the suffering caused by the terrible costs of World War I. We read of shared common experiences of isolation, hard work, few amenities, limited educational opportunities, restricted social life and heavy responsibilities, but also of satisfaction over skills mastered and work performed. Though often hard, children’s lives reflected a hopeful and expanding future, and their letters recount their skills and determination as well as family lore and community histories. Children both make and participate in history, but until recently their role has been largely ignored. In “I Want to Join Your Club,” Lewis provides direct evidence that children’s lives, like adults’, have both continuity and change and form part of the warp and woof of the social fabric.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.066
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0660.037

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 head0.050
GPT teacher head0.320
Teacher spread0.270 · 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
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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