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Record W2596313362

P06. The Western Archive of Dialects and Languages

2017· article· en· W2596313362 on OpenAlexaboutno aff
Michael Iannozzi

Bibliographic record

VenueScholarship@Western (Western University) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and language evolution
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsComputer scienceHistoryPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Traditionally, once fieldwork in a community was complete, the researcher left, and the participants never saw what became of those recordings and digitized materials. The Western Archive of Dialects and Languages (WADL) is a new project between the author and Western Libraries to ensure that the community of research directly benefits from sharing their stories, materials, and time.\nThe materials, dialect, and stories are preserved in a permanent and accessible, online archive. This ensures that future generations, wherever they live, will be able to access the heritage and history their family members shared with researchers.\nI have already digitized, and made public, children’s grade-school report cards from Mussolini-ruled Italy, cassettes of self-penned folk songs sung around campfires, and stories of daily-life in depression-era rural Ontario. These may not have been significant at the time, but are now windows into a time that has long ceased to exist.\nThe goal of this research is to show the importance of heritage—a significant part of anyone’s identity. Integral to this is changing the perception that the ‘every day’, is unimportant. On the contrary, our only access to the past is through the daily lives of those who came before us. We don’t connect to our shared history through the famous or the one-of-a-kind; but rather it is through the ordinary that we experience the entirety of a time and place.\nBy working alongside language communities, I am creating an archive to preserve, promote, and pass on the heritage and histories of all.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.084
GPT teacher head0.301
Teacher spread0.217 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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