Bibliographic record
Abstract
As is case in many states, Arizona lacks adequate funds for teaching of social studies in middle school, so teachers with specialties in other fields of study are often called upon. Last fall, our principal informed several of us seventh grade teachers that we would be teaching social studies curriculum. teach seventh grade math, I thought to myself, How am I going to be able to teach seventh grade social studies? Dividing up sections was easy--until we came to taboo section: world religions. After some grueling debate, team decided that I was most qualified candidate to teach this section, only because I have a very diverse religious and cultural background. (I was born in Nairobi, Kenya, to a Muslim Pakistani father and a Christian Finnish mother.) I was apprehensive because topic of religion is very controversial. Indeed, I was surprised that it was in district's curriculum at all. I imagined parents calling up to ask questions like, Why are you teaching my child religion? Preparation The Scottsdale District's curriculum states that students will compare and contrast the world's five great religions: Hinduism, Buddhism, Judaism, Islam, and Christianity. Students are to examine origins, founders, main beliefs, and customs of each religion. Religious conflicts, as well as current events issues, are to be explored. This seemed a tall order to fill. I started reading materials for course, but I also considered who I would be teaching. My class consisted of twenty-nine students of varying ethnic and national background, including seventeen Americans of Northern European and one of East Indian background, one Native American, eight Hispanics, one Iraqi, and one Chinese. The class met for forty-five minutes each day. A preservice teacher from Ottawa College joined adventure. At outset, I decided that, throughout year, I would gather data about what sense my students were making of curriculum: I would write in a journal (my preservice teacher also kept a journal), interview students, and analyze their work. For example, I coded data according to four categories: (1) Lacking Previous Knowledge; (2) New Ways of Thinking; (3) Making Connections; and (4) Openness/Awareness. At conclusion of course, I reviewed these data to see what changes had occurred in my students. In this article, I would like to describe some of process of learning that went on, and to highlight some of interesting moments for students, and for teachers, as we learned together about world religions. The Adventure Begins The first thing I had to do was find out where my students were coming from. What did they already know (or think they knew) about topic? What background did they have? I gave my students a questionnaire, asking them to describe each of world's five great religions. The results of survey did not surprise me. Most students knew a lot about current practice of Christianity. Many gave details like, know a lot because it is my religion and I go to church every Sunday. As for Judaism, their statements were less precise: They have a fun game called dreidel and They are originally from Israel. When it came to Buddhism, Hinduism, or Islam, most students simply responded, don't know anything about this. However, one student had an extensive amount of insight about Hinduism and another about Islam; each came from a family that practiced that religion. As I had expected, students who practice a particular religion had some previous knowledge of its main aspects, but if a religion was not their own, they did not seem to have much awareness of it. Sudden Tragedy Students had taken notes from hearing lectures, watching videos, and viewing things that I brought to class, like some Hindu holy books, saris, and pictures of a Hindu wedding. …
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".