Construction of Stance through the Use of Retrospective Labels by American and Turkish Academic Writers
Bibliographic record
Abstract
In parallel with the recognition of the importance of writer presence in academic texts, there has been an increasing interest in writer stance. Yet, very little of this research has been devoted to the construction of stance through retrospective labels. Driven by this need, this study aims to investigate the construction of stance through retrospective labels by American and novice Turkish writers in their texts. Using a corpus-based methodology comprising of quantitative and qualitative procedures, this study analyzes the frequency counts of stance through retrospective labels and the functions associated with them. The results of this corpus-based research have revealed similarities as well as some marked differences between the two corpora. It seems that in addition to proficiency in English, educational background of novice Turkish academic writers have an impact on their construction of stance through retrospective labels. I suggest that the strategic employment of retrospective labels to create stance is a valuable rhetorical strategy for academic writers to construct convincing arguments.
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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 | 0.000 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".