Listening across Dialects: Oral histories in service of linguistic social justice
Bibliographic record
Abstract
Digital Humanities Session (if it does not overlap with SLCE Despite challenges from various sources, higher education programs increasingly address equity and social justice (Kelly & Brandes, 2010). Even as we applaud this trend, we recognize with US-based dialect researcher Walt Wolfram that, "dialect prejudice remains as one of the most resistant and insidious of all prejudices in our society" (2014: 24) and that students often share these damaging prejudices. Preconceptions and misconceptions of others' backgrounds influence our interactions, sometimes even before we meet one another (Riley et al., 2012) and the negative impact others’ expectations for speakers of stigmatized vernaculars is uncontested within linguistic communities (e.g., Godley & Minnici, 2008; Rickford & Rickford, 2005). Many otherwise concerned and equity-advocating humans unintentionally discriminate against individuals who speak in non-standard ways. Because we cannot fight prejudice and discrimination we do not see, this presentation centers on a course that leads students to discover the regularity, logic, and intrinsic value of non-standard varieties of English. While the first iteration of the course proved effective in leading students to understand cognitively that dialects are natural, logical, and intelligent varieties of language, evidence from student comments during and after the semester suggested that many students retained linguistic prejudice. Long-held bias remained despite cognitive recognition that it violated logical principles. Seeking to extend the impact on students to unconscious, affective levels, the I now offer students the opportunity to replace a traditional research paper with an oral history project which guides students through the collection and curation of oral histories of people in the local community who speak non-standard varieties. This presentation focuses on the oral history component of the course which leads students to listen across cultures as they experience the logic and regularity of language outside the mainstream through interpersonal interaction and hearing the stories of people who speak differently from themselves. Historically, I have had challenges making this project authentic, as oral histories are meant to be shared publicly. The Voices of the Upstate Digital Archive make this authentic publication possible. References Godley, A., & Minnici, A. (2008). “Critical Language Pedagogy in an Urban High School English Class.” Urban Education, 43, 3, 319-346. Kelly, D. M., & Brandes, G. M. (2010). “Social justice needs to be everywhere”: Imagining the future of anti-oppression education in teacher preparation. Alberta journal of educational research, 56(4), 388-402. Rickford, John R., and Angela E. Rickford. 1995. “Dialect readers revisited.” Linguistics and Education, 7, 2, 107-28 Riley, Tasha, and Charles Ungerleider. "Self-Fulfilling Prophecy: How Teachers' Attributions, Expectations, And Stereotypes Influence The Learning Opportunities Afforded Aboriginal Students." Canadian Journal Of Education 35.2 (2012): 303-333. Wolfram, W., Adger, C. T., & Christian, D. (1999). Dialects in schools and communities. Routledge.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".