Influence of Text Mediation Upon the Academic Identities of Novice EAP Authors: Review and Prospect
Bibliographic record
Abstract
In order to provide an overview of the research on the influence of text mediation upon the academic identity of EAP authors, this paper reviewed the available literature on academic identity, identity construction in academic writing, text mediation and its influence upon academic writing. It was found that different mediators indeed play roles in the drafting or publication of manuscripts by English for Academic Purposes (EAP) authors, especially novice ones. The final version of a manuscript was thus formulated by both the named author(s) and many unnamed others, which covers the metadiscourse employed in the very manuscript. Since metadiscourse helps to shape the identity of the author, the mediators correspondingly exert influence upon the identity construction of the academic authors. However, little attention has been paid to the impact of text mediation upon the author’s identity. In view of this, this paper proposed the prospects for further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".