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Record W7005852436

A Song for Every Cow She Milked..." Sharing the Work and Sharing the Voices in Gaeldom

2013· article· en· W7005852436 on OpenAlexaboutno aff

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2013
Typearticle
Languageen
FieldMedicine
TopicAndrographolide Research and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsClanCustodiansNobilityPresentation (obstetrics)Work (physics)Folkloristics
DOInot available

Abstract

fetched live from OpenAlex

Throughout the history of the Gael, both in Scotland and overseas, every aspect of life had its songs.Whether composed by the highly literate clan bard or by the non-literate farm servant, a huge wealth of songs was handed down from generation to generation.Traditional settings differed between the nobility and the ordinary folk, yet the songs were equally preserved in the clan chieftain's great-hall and the humble thatched cottages that were the taighean ceilidh (visiting houses).Events (such as weddings, births, feuds, battles, emigration, death) and memorable individuals were celebrated (or mourned) in song.Almost every kind of work had its songs, especially daily or seasonal labour done to a particular rhythm, including milking, churning, spinning, waulking (fulling) hand-woven cloth, reaping, or rowing.At the end of the day's toil, songs in the taigh céilidh were the expectation and right of everyone, along with an opportunity to learn the tradition from established singers and custodians of centuries of knowledge.This presentation discusses the range of songs and their function, from the most ancient "lay" through to modern compositions.Example of Gaelic songs from Scotland and Newfoundland (both recorded and sung by the presenter) will demonstrate points made through the paper.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.005

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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 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
Published2013
Admission routes1
Has abstractyes

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