A Case Study on the Value of Humanities-Based Analysis, Modes of Presentation, and Study Designs for SoTL: Close Reading Students’ Pre-Surveys on Gender-Inclusive Language
Bibliographic record
Abstract
Close reading has long been heralded as a humanities-specific methodology with significant potential for SoTL. This essay fills a gap in SoTL literature with a full case study demonstrating what, exactly, close reading shows us about our data that social science-based quantitative and qualitative analyses may not. Close reading-based analysis of first-year writing students’ pre-surveys on gender-inclusive language entails attention to the interrelated form and content of students’ self-reflections. This analysis reveals nuances and complexities that, if overlooked, would result in inadvertent misrepresentation of the data. This case study responds not only to calls for humanities-specific SoTL methodologies but also to related calls for greater legitimation of diverse forms for SoTL dissemination, some of which originate in the humanities. It is therefore cast as a reflective essay based on its author’s scholarly personal narrative (SPN) as a new, humanities-based SoTL researcher. Finally, this case study demonstrates the value of flexible, deliberately unscientific study designs that are responsive to emergent conditions but foreign to SoTL’s dominant social science paradigm. As guides to instruction, pre-surveys are necessary complements to pre-quizzes: learning what students think they know about a concept or skill, their attitudes towards it, and their contexts of prior learning about it—not just their knowledge of it, which is all pre-quizzes can tell us—is an important precursor to effective instruction. But maximizing pre-surveys’ potential to guide instruction requires flexible study designs so we can change our pedagogy, including our study’s “intervention,” if necessary, on the fly.
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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 | 0.085 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| 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".