Exploring International College Students’ Affective Experiences in a Biotechnology Pedagogical Context
Bibliographic record
Abstract
The purpose of this paper is to explore affective experiences of international college students and their instructor in a biotechnology technician program, particularly attending to notions of ‘‘teaching desire” (McWilliams, 1997). “Teaching desire” carries with it a double articulation: a desire to teach and teaching as “seducing students’ desires” (Zembylas, 2007). Desires clearly motivate pedagogical practices and are central to promises of education in enacting and (ultimately disprivileging) certain ways of being, thinking, and valuing. Focusing on international Indian college students and their instructor’s practices and discourses inside a microbiology lab, I examine how “teaching desire” works to produce particular subjectivities with science and technology (education). ‘Teaching desire’ is further complicated by international students’ desires to live permanently in Canada pre-conditioned by their ability to secure a Canadian degree, work permit, and a job. I discuss underlying assumptions governing how international students can come to be-long to Canada through their program. Finally, I shed light on transformative potential of a “pedagogy of desire” (Zembylas, 2007) in seducing students to enter in (new) relationships with science and technology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".