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Record W4386417842 · doi:10.5430/wjel.v13n8p63

Writer-Based and Reader-Based Prose in the Author’s Development as a Newspaper Columnist

2023· article· en· W4386417842 on OpenAlexvenueno aff
Mallika Vasugi Govindarajoo, Shorouk Aboudahr, Jayakaran Mukundan

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsCredenceNewspaperPeriod (music)PsychologyContent analysisColumn (typography)PerceptionLiteratureSociologyMedia studiesComputer scienceArtAestheticsSocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the researcher’s development as a columnist, particularly from the perspectives of Writer-Based and Reader-Based prose, and to identify the topics that have been recurrent themes during the 20-year period of education column writing. An in-depth examination of the researcher’s 210 published column articles (Teacher Talk, The Star. 2002-2021) was carried out with an analysis of content to compare the extent of Reader-Based Prose and Writer-Based Prose as well as topics or issues pertaining to teachers that have been consistently featured throughout the 20 years.Semi-structured interviews were conducted with six participants for the purpose of triangulation. The study revealed that over the period of 20 years, there was a gradual but definite shift towards greater Reader-Based Prose and stronger writer’s ‘presence’ in the column articles. Although elements of Writer-Based Prose were present in particular instances, there was a stronger pattern of Reader-Based Prose in the researcher’s writing, especially in articles written after 2005. Readers could identify with the characters and settings and draw personal meanings from the issues highlighted in the articles th us providing credence to Reader Response Theory. The issues or topics that featured most in the articles and that were most often repeated throughout the 20-year period were teachers’ perceptions of policies and professional issues and special school events. The same topics were the main emerging themes during the interviews thus providing confirmation that most issues experienced by Malaysian school teachers have remained the same over the past twenty years.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.783
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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