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Record W4221026575 · doi:10.5539/ells.v12n2p46

The Importance of Instapoetry in Light of Dominant Forms with Special Reference to Rupi Kaur’s Milk and Honey

2022· article· en· W4221026575 on OpenAlexvenueno aff
Yasser K. R. Aman

Bibliographic record

VenueEnglish Language and Literature Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryPoeticsArgument (complex analysis)SociologyOrder (exchange)Marxist philosophyAestheticsLiteratureSocial sciencePhilosophyLawArtPolitical scienceEconomicsPolitics

Abstract

fetched live from OpenAlex

Recently Instapoetry, a form of minimalist poetry, has emerged as a result of using Instagram as a platform for expression. It has strived to gain validity since many of Instapoets have gained millions of followers who have approved this kind of writing which, most of the time, is accompanied by advertisements that symbolize the hidden economic agenda that controls who will get published. However, Instapoetry has been and is still being faced by a wave of disapproval. The paper’s argument is to verify the validity and investigate the reliability of Instapoetry, an emergent subgenre, by measuring it against the dominant literary canon which includes areas of the residual. The paper sheds light on how the Marxist economic approach to literature reproduction affects this newly-exercised type of poetry; to what extent Instapoetry can be considered a mirror of social values, and how it can be a form of propaganda. The researcher compares theories of poetry in Plato’s Republic, Aristotle’s Poetics, Sidney’s An Apology for Poetry and Shelley’s A Defence of Poetry in order to formulate measurements, a paradigm, against which this, and other future types, of poetry can be tested, putting in mind the economic factor that has changed the map of publishing houses in the UK and the USA in 2017 for example.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.316

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 studies0.0000.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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