Challenges of Nigerian Accounting Postgraduate Students in Taking up Stance in Ph.D. Theses in Bayero University, Kano, Nigeria
Bibliographic record
Abstract
This paper investigates the use of stance linguistic features in accounting Ph.D. theses in a Nigerian university. We adopted a mixed-methods approach by combining a textual analysis of the theses and explored the context of writing of the participants similar to Swale’s textography approach. We compiled three corpora: Bayero University corpus of six accounting Ph.D. theses (BUK corpus); a United Kingdom corpus of six accounting PhD theses (UK corpus) and a corpus of eleven journal articles of accounting (JAA corpus). The results of textual analysis indicate that there is a higher frequency of hedges in all the three corpora than other stance features, followed by boosters, then attitudinal markers, and explicit self-mention features. One striking finding from the BUK corpus is that the authors are rarely used self-mention features compared to authors from other two corpora. However, the result of the chi-square indicates that the differences among the three corpora’s use of stance features are insignificant. The contextual data suggests that non-teaching of English for specific purposes and the traditional practices of Bayero University might be some of the possible factors that constrained authors’ use of stance linguistic features. We recommend introduction of teaching English for specific purposes on postgraduate programmes in Nigerian universities.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".