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Record W6996847955

Strategy in Counting-Out: Evidence from Saint-Nazaire, France

2018· article· en· W6996847955 on OpenAlexaboutno aff

Bibliographic record

VenueIndiana Magazine of History (Indiana University) · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFolkloreEthnographyField (mathematics)MountSection (typography)Data collectionWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

In a study based on the ethnography of speaking approach, Goldstein described and analyzed various strategies used in counting-out by four-to fourteen-year old children in the East Mount Airy section of northwest Philadelphia in 1966-67.Goldstein observed that, although he had been informed orally of similar or other counting-out strategies employed in other parts of the United States, Canada, Europe, and Africa, there were to his knowledge no published accounts of these (178, n. 15).The purpose of this paper is to contribute to the crosscultural investigation of children's folklore by providing data on counting-out strategies used in Saint-Nazaire, France. 1 My field work was initially undertaken for a doctoral dissertation (Arleo 1982) involving the comparative study of counting-out rhymes in French and English.Research methods included observation, interviews and recording in play areas and in the classrooms of several elementary schools, as well as the collection of written documents (texts, drawings, descriptions, questionnaires) from children and adults.Additional collecting of counting-out and other children's rhymes has been carried out since 1982 to the present time in Saint-Nazaire and the surrounding areas. 2The Countimr-Out Ceremonv in France.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.210
Teacher spread0.180 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

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