Bibliographic record
Abstract
This study systematically investigates the English writing research in Taiwan, over the span of time from 1989 to 2008, a 19-year time period. Data collection consisted of five major sources. Guided by Juzwik et al’s (2006) study, the data were analyzed based on the general problems under investigated, the age groups being researched, the methodologies being implemented, and types of research being conducted at different grade levels. Findings revealed that writing instruction, writing and technologies and peer evaluation were the most studied problems in writing research whereas collaborative writing, error analysis, and cultural influences were the least studied problems. The most studied populations were university and senior high school students while the least studied groups were kindergarteners and adults. Most studies were conducted by using qualitative methodology. Writing and technologies was the most studied type of research among elementary school students and university students, whereas writing instruction was frequently studied among senior high school students, graduate students and adult students. The implications and recommendations that emerge out of these results provide possible agendas for writing teachers, researchers and policy makers worldwide.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".