A Math Ed Take on Humble Humour A Review of Matt Parker’s Humble Pi: When Math Goes Wrong in the Real World
Bibliographic record
Abstract
If anything, it was a few years of teaching grade eight home economics classes that made the situation very clear to me. There were the spectacular cooking disasters, like the group that while making a chocolate cake from scratch somehow switched the measurements for the salt and the sugar. Not only did they end up with a product that even a growing grade eight boy wouldn’t eat, but the cake actually erupted in the oven while it was baking. But there were also the smaller, more telling moments. I’d see a group from across the room that had come to a standstill. I’d approach and discover that kids who had been acing their math tests all year long found themselves unable to agree on the result of halving 1¾ cups of flour and were now all staring at their measuring cups in silence. Put a fraction calculation out of context on a piece of paper, these students were golden; faced with actual ingredients and tools under the flickering fluorescent lights of our home economics lab, they were flummoxed. While the Great Cake Explosion was a once-in-acareer lowlight (although it’s a good story, I ended up being the one who had to clean that oven), unfortunately, the measuring cup situation happened at least once each term, where it was apparent the students had little feel for the math they were doing back in math class.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.037 | 0.030 |
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".