A Case Study in the Application of Authentic Material Texts: Findings after Using the New York Times Monday Weekly on the United Daily News
Bibliographic record
Abstract
It is easy to have access to satellite or cable TV programs in Taiwan, and thus ordinary learners have ample opportunities to immerse themselves in an English-rich environment if they want. However, military cadets do not have enough English input because their time is limited on campus. In this study, the writer strives to figure out the best way to increases their interest in the language and relate English instruction to their real world. She used the New York Times Monday Weekly (NYTMW) in the United Daily News as a supplementary reading material during the 2005-2006 school year, and tried to analyze the mixed results. She finds that retention is increased if the selected material is relevant to learners’ lives. Key words: NYTMW, New York Times, reading assessment, authentic material texts Resume: Il est facile d’acceder aux programmes de satellite ou de televison par câble a Taiwai, et ainsi les apprenants disposent de nombreuses opportunites pour s’immigrer dans un environnement anglais s’ils en ont envie. Cependant, les eleves des ecoles militaires n’ont pas assez de contact avec l’anglais parce qu’ils passent leur temps au campus. Dans cette etude, l’auteur essaie de trouver le meilleur moyen de soulever leur interet et relier l’instruction anglaise a leur vie reelle. De plus, elle trouve que la retention est augmentee si le materiel selectionne concerne la vie des apprenants. Mots-Cles: NYTMW, New York Time, evaluation de lecture, materiel authentique
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".