To Whom Are You Writing? Examining Audience in L2 Textbook Writing Activities
Bibliographic record
Abstract
A sense of audience is important in the development of student writing (Many & Henderson, 2005). Research shows students need to consider an audience’s attitudes, beliefs, and expectations to be effective writers (Midgette, Haria, & MacArthur, 2007). Therefore, students need learning opportunities in L2 classrooms to develop this ability. Yet, the incorporation of audience in L2 textbook writing activities has not been sufficiently addressed. This study examined textbook activities to whom students write based on parameters of audience influence proposed by Grabe and Kaplan (1996, 2014). Writing prompts from six high school textbooks in Saudi Arabia were analyzed. The results indicate prompts instruct students to write to a single reader, known/unknown readers, as well as write about general topics. However, prompts do not provide information for students about three parameters (age, gender, and social status) which are necessary ingredients in developing a writer’s sense of audience and play a significant role on textual variations. This study also modified a model of audience that can be used for textbook evaluation. The findings benefit textbook developers and teachers by motivating them to consider parameters of audience influence when they design lessons and materials for L2 writing classrooms.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".