Bibliographic record
Abstract
The context: All the students at Edward Waters College have to take “Introduction to Biblical Studies” irrespective of their majors. This has often presented me with challenges but the greatest challenge has been to engage students who are not religion majors. Many of these students feel that this course is not necessary and that it delays their progress in their major fields. I realized that even though class participation accounted for a quarter of the final grade quite a number of students were coming to class unprepared and their participation levels were very low. Discussions with a number of students revealed that they spent most of their time preparing for classes in their major fields and gave minimal attention to biblical studies because they did not see its use in their field of study and their career goals. After trying a number of strategies with minimal success, I decided to involve students more in the teaching process. The pedagogical purpose: I use this strategy: to engage and motivate students; to enable students to explore the connections between biblical studies and other academic fields; to build the relationship between teacher and students as we work together in preparation to teach; and to encourage students to use their skills and talents in and outside of the class environment. Description of the strategy: After every class I choose two or three students to serve as teaching assistants for my next class. I try to select students from different majors. I meet with these students a day before the class and we discuss how we will present the material. Although I provide needed leadership for the discussion, I allow enough flexibility for the students to express their views. We always discuss the connection between the topic and their major fields. I request that these students explain this connection in class. After making a decision on how we will present the material, we distribute the duties among ourselves. Teaching assistants arrive early for class and are expected to behave and dress like teachers. Why it is effective: Since I started using this strategy, I have seen a tremendous increase in students' engagement and interest in the class. Having different “teachers” in every class gives students something to look forward to. My relationship with students is enriched by this experience. Some students are not willing to participate and it takes time to encourage them. For such students I give them a small part to play even if it is distributing papers or cleaning the board. Another challenge is ensuring that subject matter presented in class is current and correct.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.261 | 0.147 |
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".