Efficacy and Agency with African American Bidialectal Students in Face-to-Face and Online Classes
Bibliographic record
Abstract
I am a minority. As an English-speaking Canadian living for years in the French part of Canada, I knew what it meant to be a minority, shunned because my accent revealed my heritage. However, when I joined the faculty at Elizabeth City State University (ECSU), one of the nation’s historically black colleges (HBCU) in North Carolina, and moved from the financial capital of Canada to small town USA, I discovered what it meant to be a racial minority based on skin color. I was pleased to find, however, that my minority status did not inspire rejection; to the contrary, I experienced acceptance from the academic community of professors and students. Not only did I exchange sky scrapers for corn fields, but also the second language of French for the euphony of African American Vernacular English (AAVE). While not all African American students identify AAVE as their dominant dialect, there are many students who are bidialectal at both HBCUs and other state comprehensive universities (SCU). The American Association of State Colleges and Universities (AASCU) represents 38 of the 106 historically black colleges and universities in the US, including ECSU. Moreover, according to their 2013 Annual Report, at least half of the student population at 76 AASCU schools are minority students, and AASCU member institutions educate around 51 percent of all minority students, including 63 percent of all African-American students. During my five years of teaching at ECSU, I have found that an instructor’s efficacy with African American bidialectical students is not based on his or her skin color or dialect; rather, it is based on the depth to which he or she understands the African American student’s culture and identity, and the implications these two elements have on a student’s agency. Determining the best strategies to promote student agency can be fraught with challenges (Czerniewicz, Williams, & Brown, 2008). In order for professors to be rhetorically effective, they must first understand that their African American students have internal contention based on racial performance that forms a juxtaposition between black identity and white “academic” expectations, and this juxtaposition will ultimately have an impact on the African American student’s approach to his or her educational 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".