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Record W2907416197 · doi:10.5539/ijel.v9n1p383

A Semantic Study of Dummy Subjects in Dickens’ Novel Hard Times

2018· article· en· W2907416197 on OpenAlexvenueno aff
Zainab Hussein Alwan

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Policy, and Dickens Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)Context (archaeology)LinguisticsComputer scienceNatural language processingSemantic propertyPsychologyArtificial intelligenceEpistemologyPhilosophyHistory

Abstract

fetched live from OpenAlex

To be required for some grammatical properties, dummy elements have lost their lexical meaning. They have no sense of their own. However, such items have meaning in context. They play essential roles in the general semantic structure of sentences. This study attempts to confirm that dummy constructions can have some semantic meaning and there is no matter how abstract they are. It also highlights the functions of inserting these elements to satisfy the structural and semantic needs in Charles Dickens’ novel Hard Times.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0020.004
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.349
Teacher spread0.314 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicMigration, Policy, and Dickens StudiesFrench-language works237,207