At the Edge of Language: Truth, Falsity and Responsibility inTeacher Education
Bibliographic record
Abstract
I got into some problems with the administration. I tried to split up one group because they were unevenly matched. The previous day they were just killed so I tried to split them up and I didn’t explain my rationale beforehand or while I was doing it.... So I tried to move them over and somehow the students collaborated that they wanted to stay on the same team.... I found out this after...the fact that they actually switched ... and went back to their own teams...like they were before.... So I expressed my frustrations, you know, silently say[ing] I just don’t like it. [Laughing] So they saw me do this and I chewed out a student beforehand.... And so she obviously got really upset about it and the students who actually collaborated to form their own group again...talked her into...going to the administration and filing an incident report that I had driven a student to tears and swore at them.... So I’m in the lunchroom interacting with the staff ... and the Principal comes up and says, “What happened in class today?” I didn’t mention what I expressed in frustration and I said nothing like this happened ... because I knew what it was going to look like.... (Reza, student teacher)
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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 | 0.037 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.110 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".