Literature reviews, citations and intertextuality in graduate student writing
Bibliographic record
Abstract
Literature reviews are a genre that many graduate students do not fully understand and find difficult to write. While the genre, language and rhetorical moves of literature reviews are widely researched, less research focuses on citation use in literature reviews. Teaching students ‘how-to’ write the literature review through explicit genre awareness is not enough. What is needed, is a focus on the discursive nature of citations since citations are a core ingredient in literature reviews. The complexity of citing, referencing and using sources is difficult to teach in university classrooms, especially in courses that focus on content knowledge. When it is taught, it is often imparted to students as conventions with particular organisational features around citation styles. Or it is taught within the discourse of morality and academic dishonesty that surrounds plagiarism. What is lacking is a pedagogy that relates citing to the more complex discursive practices that are implicit and deeply embedded in particular time-bound contexts. The purpose of this research was to explore citation patterns in 23 draft and final masters’ student literature review papers to better understand the ways in which students use sources in literature reviews with the aim of informing pedagogy. Findings indicate that the concept of intertextuality, specifically, transgressive intertextuality, intertextual engagement and discursive intertextuality can help with the teaching and learning of literature reviews and citation use.
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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 | 0.070 | 0.339 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".