The Academic Dilemma on the Use of Proof Readers in Academic Assignments
Bibliographic record
Abstract
It is a well-known idea that Non-Native English Speakers (NNES) often try to find ways to assist writing. Perhaps, the most common assistance would be feedback from their supervisors or support from their peers. However, certain students with means, would go the extra mile of employing proofreaders to help improve their writing. This study is part of a longitudinal narrative study involving five international postgraduate students in a UK university where the theme of proofreader and/or proofreading had become an academic dilemma on whether it should be permitted at all. The findings showed both positive and negative assumptions from the participants. A participant who scored well with the help of proofreaders learnt the university’s writing conventions from her ‘mistakes’. Another participant who was academically weaker however, expected her writing to be ‘translated’ into the university’s writing conventions along with grade improvements. Other participants deemed such gesture as immoral and blamed the university for not banning such services, putting less financially able students at a ‘disadvantage’.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".