Writer-Based and Reader-Based Prose in the Author’s Development as a Newspaper Columnist
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".