Bibliographic record
Abstract
To communicate effectively in any language, one needs to be competent in using four language skills; that is, reading, listening, reading, and writing- and accordingly the integrative skills instruction has come into prominence in L2 teaching. EFL teachers’ beliefs, ideas, perceptions, attitudes are known to have a significant impact on their profession. Termed as “teacher cognition” by Borg (2003), the mentioned beliefs or perceptions are not directly observable. In this study, it is argued that teachers’ perceptions about four language skills as a part of their teacher cognition will give insight to their instruction. This is a qualitative study which aims at finding out the prospective EFL teachers’ perceptions about writing skill through metaphors. The participants included the undergraduate students studying English as a foreign language at two universities, Istanbul and Amasya. The data were analyzed with the content analysis technique. The findings revealed that prospective EFL teachers had various views regarding the nature of writing. These perceptions, either positive or negative, will influence their future practices; thus, it is essential that the awareness of prospective EFL teachers should be sharpened to help learners to understand the complicated nature of writing and proceed in writing.
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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".