MétaCan
Menu
Back to cohort
Record W2552668272 · doi:10.1177/028072701203000102

“101 Years of Mine Disasters and 101 Years of Song: Truth or Myth in Nova Scotia Mining Songs?”

2012· article· en· W2552668272 on OpenAlexaffabout
Joseph Scanlon, Nick Johnston, Allison Vandervalk, Heather Sparling

Bibliographic record

VenueInternational Journal of Mass Emergencies & Disasters · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsCape Breton UniversityCarleton University
Fundersnot available
KeywordsNova scotiaContext (archaeology)HistoryMedia coverageMythologyEvent (particle physics)LiteratureMedia studiesArtSociologyEthnologyArchaeologyClassics

Abstract

fetched live from OpenAlex

It is generally accepted that the majority of responses to a disaster in social media sources misrepresent what actually occurs in such an event. Over the last century, mine disasters occurring in Nova Scotia have generated numerous responses in the form of folk songs. The purpose of this study is to determine if these folk songs, unlike other forms of popular culture, accurately portray the events and context surrounding these disasters while also examining how they describe human responses to disasters. The findings show that these folk songs in contrast to other media, books and movies do provide a generally factually and contextually accurate view of the disasters, with a focus limited to the events of the disaster itself, the rescue efforts, and the dead and trapped. The possibilities for why this is true are then considered, including looks at the nature and origins of the songs, the response of composers to the disasters, and how the media response to the disasters affected the songs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

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

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 head0.033
GPT teacher head0.319
Teacher spread0.286 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Mass Emergencies & DisastersSame topicDisaster Management and ResilienceFrench-language works237,207