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Record W2584630867 · doi:10.1017/s1360674316000551

The changing<scp>future</scp>: competition, specialization and reorganization in the contemporary English future temporal reference system

2017· article· en· W2584630867 on OpenAlexaff
Derek Denis, Sali A. Tagliamonte

Bibliographic record

VenueEnglish Language and Linguistics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of TorontoUniversity of Victoria
Fundersnot available
KeywordsGrammaticalizationAnimacyLinguisticsVariety (cybernetics)Constraint (computer-aided design)Subject (documents)VerbCompetition (biology)MainstreamSentenceConstruct (python library)Variation (astronomy)HistoryPsychologyComputer scienceArtificial intelligencePolitical sciencePhilosophyMathematicsBiology

Abstract

fetched live from OpenAlex

The English future temporal reference system has long been recognized as a variable system undergoing change. The main variants in contemporary English ( will and be going to ) have both been argued to have gone through (and to potentially still be undergoing) grammaticalization. At the same time, be going to has been gradually increasing in frequency relative to will over the last 500 years. However, investigation of the ongoing development of this system has been sparse. This article makes use of a large contemporary sociolinguistic corpus of a mainstream variety of North American English and the apparent-time construct. Several factors that have been implicated in the development of this system (Sentence Type, Clause Type, Proximity, Verb Type, and the Animacy and Grammatical Person of the Subject) are considered and a multiplex series of changes are uncovered. Underlying an overall, albeit slow, change in frequency towards be going to , we find evidence for specialization of one or the other variant in different linguistic contexts, neutralization of a constraint consistent with ongoing loss of variant nuances through semantic bleaching, and the persistence of constraints consistent with morphological doublet competition.

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)0.002
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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.

Study designTheoretical or conceptual
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

Citations28
Published2017
Admission routes1
Has abstractyes

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