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Record W4386313248 · doi:10.1515/9780888647931

Heavy Burdens on Small Shoulders

2009· book· en· W4386313248 on OpenAlexaboutno aff
Sandra Rollings-Magnusson

Bibliographic record

VenueUniversity of Alberta Press eBooks · 2009
Typebook
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsShouldersEnvironmental scienceMedicine

Abstract

fetched live from OpenAlex

The phrase "child labour" carries negative undertones in today's society. However, only a century ago on the Canadian Prairies, youngsters laboured alongside their parents' working the land, cleaning stovepipes, and chopping wood. By shouldering their share of the chores, these children learned the domestic and manual labour skills needed for life on a Prairie family farm. Rollings-Magnusson uses historic research, photographs, and personal anecdotes to describe the kinds of work performed by children and how each task fit into the family economy. This book is a vital contribution to western Canadian history as well as family and gender studies.

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 score0.055
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0550.013

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 head0.037
GPT teacher head0.246
Teacher spread0.208 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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