Affective Contours of Two College Microbiology Laboratories: Potentializing International Students' Be-longings with Science, Education and Canada
Bibliographic record
Abstract
This dissertation explores affective politics of two college microbiology laboratory courses. Drawing from studies of affect, care and politics of aesthetics, I examine how bodies of science education and science education bodies are reproduced and transformed through particular affective attachments or be-longings. Accounting for student demographics in the two courses observed — International students from India — my analyses further situate those be-longings in relation to what it means to have ‘the good life’ in Canada. While one course (project 3) depicts affectivities of precarious living and being (for various participants involved), the other course (project 2) gestures to how a disciplining of the biotechnician body meets histories of subjugation that entangle nature (simplistically here, bacteria) and women. Pedagogies of the college microbiology laboratory (existing within larger networks) work affectively to coalesce epistemic privileges of science (as hard, objective, masculinist) with production of more-than-human subjects for capitalist markets (in this case, food and pharmaceutical industries). Seeking affect through its productive (and not merely reproductive) intensities in educational situations, I approach my contexts of study as already ripe with pedagogical openings for being, thinking and caring differently within science and science education. I further describe such openings in relation to a new pedagogical approach, ‘STEPWISE’ (Bencze, 2017), that creates some interruptions in this context, potentializing new be-longings. Particularly evident in project 3, STEPWISE turns into a ‘warm’ pedagogy that meets with friendship flows and possibilities for being in renewed relationships with science, knowledge, bacteria, education and the job market. This research contributes to studies of emotions/affect in science education from a political perspective. A proposed direction for future research seeking productive ways to engage with emotions/affect in science education may start by contaminating cultural analyses of science education with an affectivity lens that can open new horizons for be-longing with science education and research. For instance, what could be some pedagogical possibilities for a science education that departs from a main preoccupation with ‘critical thinking’ towards centering ‘critical care’?
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".