MétaCan
Menu
Back to cohort
Record W2619265518 · doi:10.1215/00031283-4153153

Oral Histories as a Window to Sociolinguistic History and Language History: Exploring Earlier Ontario English with the Farm Work and Farm Life Since 1890 Oral History Collection

2016· article· en· W2619265518 on OpenAlexaffabout
Derek Denis

Bibliographic record

VenueAmerican Speech · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVariation (astronomy)Oral historyCasualLinguisticsHistorySociologyInterviewSociolinguisticsPsychologyAnthropologyLawPolitical science

Abstract

fetched live from OpenAlex

Archival oral histories provide sociolinguists with invaluable empirical insight, particularly with respect to studying vernaculars of the past. Because the goal of the oral history methodology is to capture authentic, first-person accounts, the recordings are comparable to sociolinguistic interviews. In this audio feature, the Farm Work and Farm Life Since 1890 Oral History project, which contains interviews with elderly farmers in Ontario, Canada, born between 1890 and 1920, is used to exemplify the utility of oral histories, particularly for variationist sociolinguists. The audio feature first demonstrates the parallels between sociolinguistic interviews and oral histories: both exhibit Labovian “casual speech” and high-emotive topics, both diminish the role of the interviewer, and, perhaps most importantly for variationist sociolinguists, both contain sociolinguistic variation. The remainder of the audio feature highlights some of this variation, including negative concord, was/were variation, and discourse like, and closes with a discussion of two revelations about the sociohistorical and sociolinguistic nature of the speech communities examined.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.256
Teacher spread0.222 · 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 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

Citations26
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueAmerican SpeechSame topicLinguistic Variation and MorphologyFrench-language works237,207