Opening Up Contested Spaces: Interdisciplinary Writing at an HBCU
Bibliographic record
Abstract
Inequalities in academic writing are not uncommon in higher education and become more complex when we look at the landscape of historically black colleges and universities (HBCUs), which serve a large number of first-generation Black students. HBCUs serve minority students and provide them a cultural connection that they often do not achieve at predominantly white institutions. Such first-generation students face a range of challenges and graduate at lower rates than other student. In terms of academic writing, such students often struggle to develop an academic identity and voice. At Johnson C. Smith University, an HBCU in the heart of Charlotte, North Carolina, all students, regardless of major, are required to complete a senior investigative paper. Many students struggle with this graduation requirement for a variety of reasons, ranging from inexperience with academic writing, lack of interest in the topic, and poor writing mechanics skills A goal specifically in the Interdisciplinary Studies Department is to have students develop a topic they find interesting and engaging. Many IDS students choose topics that address inequalities they have encountered and endured in their life or ones that are specific to their demographic (age, race, gender, sexual orientation, etc). When students are able to research and write about topics they are passionate about, their writing shows marked improvement as they develop a writing voice. As Bean (2011) notes, sometimes it is beneficial to set aside the formal academic writing expectations and focus on the content and context of a paper. This provides students with the message that what they are researching is valuable and they gain confidence in their research skills and thus their critical thinking skills. Bean also notes that when we focus on content rather than sentence-level correctness, the result is often a well-written paper, or one that is improved from the previous drafts. This article focuses on specific lessons learned from our experience working with HBCU seniors and how to apply the practices of content feedback to promote academic writing to help close the gap of academic inequality that many students experience.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.064 | 0.017 |
| Scholarly communication | 0.032 | 0.014 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".