Affect, Alpha Function, and the Very Small: A Reconsideration of Teacher Workload
Bibliographic record
Abstract
In this paper, I draw together myriad theoretical and philosophical sources to think through the intensification of emotion amid and emerging from the COVID-19 pandemic. I begin with three narratives from my own teaching and learning, which ground the subsequent conversation. I then characterize the current movement in educational theorizing known as the affective turn. The affective turn, I suggest, attunes educational inquiry to small, yet vital, moments of classroom interaction often taken for granted in public education. Toward considering those vital moments in more nuance, I discuss psychoanalyst Wilfred R. Bion’s notion of the alpha function – a nonconscious digestion of emotion we perform for others when they are overwhelmed. When coupled with Nel Noddings’ evocation of the ethics of care in education, the alpha function offers an understanding of the hidden emotional labour in teaching. This hidden dimension of the teacher’s task, the portion of the job that deals in regulating our own emotions and in helping students make sense of theirs, I suggest, is becoming more difficult amid the affective situation of the COVID-19 pandemic. I conclude the paper by gesturing toward a threefold response to be taken up more fully elsewhere: humility before the task of teaching, a reverence for the work of feeling, and a willingness to organize toward a more caring school system.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".