MétaCan
Menu
Back to cohort
Record W3201996901 · doi:10.1558/jalpp.21053

Mobilizing knowledge

2018· article· en· W3201996901 on OpenAlexaboutno aff
Tosh Tachino

Bibliographic record

VenueJournal of Applied Linguistics and Professional Practice · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArgument (complex analysis)CitationSituatedGovernment (linguistics)CommissionLinguisticsSpeech actFunction (biology)Indirect speechPolitical sciencePsychologySociologyComputer scienceLawArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Many linguistic studies have analyzed the ways in which reported speech is used to mobilize knowledge in academic writing, but there have been far fewer such studies of knowledge mobilization in non-academic genres. This study analyzes the functions of reported speech in a Canadian quasi-judicial public inquiry report, a genre that is intertextually situated between research genres (through academic expert witnesses) and policy genres (through its role in making policy recommendations to the government). All instances of explicitly marked citation and reported speech in the commission report were identified and coded by function. The findings show citation and reported speech had specific functions that contributed to knowledge mobilization by discursively creating evidence, transporting worldviews and values, and changing knowledge status in the legal genres. The analysis also raises theoretical questions in linguistics, resulting in the argument that reported speech is not a static, formal category but a discursive status negotiated by the participants.

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)0.017
metaresearch head score (Gemma)0.069
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.069
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.005
Science and technology studies0.0060.013
Scholarly communication0.0130.011
Open science0.0030.015
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.352
Teacher spread0.322 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Linguistics and Professional PracticeSame topicDiscourse Analysis in Language StudiesFrench-language works237,207