Reflective Discourses in the Classroom: Creating Spaces Where Students Can Change Their Minds.
Bibliographic record
Abstract
We are discussing Tobias Wolffs story Say Yes in a writing class at Wayne State University, an urban, commuter school in Detroit. In this story a white wife and her husband confront assumptions about identity, race, and love. The story's revelationor lack of onegrows out of a conversation in which the wife asks her husband he would have married her if I'd been black. There are twenty students in this class and no racial or ethnic group represents more than a quarter of this number. What is more, most identities are hybrid, such that only the Arab-Irish-American twins share a racial/ethnic background. Although we have spent almost ten weeks talking about similarities and differences in who we are, this story about love and identity raises highly charged material for of us, and the need to represent deeply held personal feelings to individuals different from ourselves complicates both our feelings and our representations. The seating arrangement in the room accurately reflects connections and tensions among students. Although students do not have assigned seats, they are sitting with their kind insofar as they can arrange We are working in a computer classroom and the machines are arranged around the room's perimeter. Chairs are turned toward the center. All but one of the light-skinned men, a mix of white and Arab Americans, occupy the south corner. The west corner is occupied by students with darker complexions. The African American women are seated in this area along with an African man and a young man whose family came to the United States from India. The north and east corners are more diverse. Most students in these groups are firstor second-generation Americans. They have roots in Bangladesh, Haiti, the Philippines, Mexico, the Middle East, and japan. These groups are predominantly female. The arrangement by gender makes it seem as statements made by individuals come from a group, and at the outset this both enhances and cripples discussion. It makes it possible for the speaker to distance himself or herself from personal implications of what is said, but it also gives talk the feel of hard-edged stereotyping-men are like this, women are like that. Initial talk from participants is limited to safe answers about and marriage: all of us are the same; race shouldn't have anything to do with it. These abstract answers are couched in cultural rhetoric to avoid both personal confrontation and real communication (Fox). What is more, safe answers allow students to evade implication in the sexist, racist perspectives confronted in this story. As a feminist teacher, pressing students toward open, earnest questioning of oppres-
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".