Harnessing Sources in the Humanities: A Corpus-based Investigation of Citation Practices in English Literary Studies
Bibliographic record
Abstract
Integrating outside sources for rhetorical purposes is an essential element of academic writing; yet doing so effectively can be problematic for academic writers. While corpus-based research into science writing has provided valuable insights into how published authors work with sources, citation practices in the humanities have remained largely unexplored. This paper analyzes citation conventions in a 35-article literary studies corpus and contextualizes its findings within previous research, thereby revealing distinctive writing practices in the field. Important findings include that literary studies authors cite relatively less and favor quotation over paraphrase and summary, unlike writers in previously-examined fields. As well, their syntactic integration of references and reporting verbs substantially differ. This research problematizes generalizations about humanities writing and questions assumptions regarding whether extensive commonalities exist between humanities and social science writing. The results provide further support for discipline-specific writing instruction and underline the need for further research into humanities writing practices.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| 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".