Citation Practices in the Literature Review: A Contrastive Study of Sudanese and British Applied Linguistics Ph.D. Theses
Bibliographic record
Abstract
Manifest intertextuality is a fundamental aspect of all academic discourse, and, hence, this study purports to explorethe myriad functions of citation in a representative and contrastive corpus drawn from 20 Literature Review chaptersin the domain of Applied Linguistics, and equally divided among Ph.D. theses successfully defended in Sudan andBritain. A variety of typologies were utilized to elicit citations, including Thompson’s (2005) classification ofintegral and non-integral citations, together with Hyland’s (2002) designation of denotative and evaluative functionsassociated with reporting verbs. Groom’s (2000) and Petric’s (2007) notions of averral and attribution, propositionalresponsibility and knowledge transformation also inform this investigation. Results indicate that the densedeployment of citations and the predilection both corpora have for integral structures, verbatim quotations andpresent active Discourse reporting verbs are largely dictated by the discursive and human-imbued nature of AppliedLinguistics. On the other hand, the findings reveal that Sudanese candidates formally and functionally employcitations in manners markedly different from their British peers. Thus, the Sudanese corpus is characterized byblatant errors, repetition and awkwardness in both documenting sources and reporting the findings of research.Moreover, naïve unwarranted quotations and authorial evaluations were ubiquitously observed, as compared to theBritish corpus. More significantly, there were ample variations in the way in which the two groups conceive of therole of the Literature Review. While the British adopted a range of Writer-oriented and metadiscoursal strategies toamalgamate and integrate the cited materials within their mainstream arguments, the Sudanese candidates werestrictly concerned with unmediated and uncontested attribution of ideas to their authors. Such is the synthetic natureof the resultant type of this Literature Review that the writer’s textual voice is submerged under the sheer burden ofsuccessive descriptive citations, thus eclipsing almost all of the objectives of this chapter in critiquing sources andsubordinating the cited literature to the overarching transformative perspective of the thesis writer. The Discussion isilluminated through extensive quotations from the two corpora.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.026 | 0.031 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".