Quantitative Literary Analysis & AI: A New Frontier in Humanities Research
Bibliographic record
Abstract
My presentation focused on pedagogical and analytical framework at the intersection of computational methods and literary studies. It was delivered as part of the prestigious LUNE Fellowship series where I had the privilege to be among the LUNE faculty that mentored graduate students in Nigeria on the intersection of AI in social sciences and humanities and explores how large language models, prompt engineering, and corpus linguistics are reshaping the future of African and global literary scholarship. Drawing from a wide range of theoretical traditions and tools, the presentation provides an empirically grounded roadmap for using AI for literary analysis. Key features include: Quantitative linguistic analysis of Things Fall Apart by Chinua Achebe Original prompts for feminist, postcolonial, and ontological reading strategies Insights on African language corpora and early digital humanities pioneers Demonstrations of prompt engineering’s transformative power in literary inquiry Personally, I think you would find this as a good resource for scholars, students, and digital humanists seeking to bridge literary tradition with AI innovation.
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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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".