On Superiority of Simple Solutions to Complex Problems and Other Fairy Tales
Bibliographic record
Abstract
ABSTRACT: The simple is desirable; the complex is confusing. The Avaluator Avalanche Accident Prevention Card (Haegeli & McCammon, 2006), a Canadian government avalanche accident preven-tion initiative, was designed to help recreationists to avoid avalanche accidents. It consists of a Trip Planner and Obvious Clues tools. The Trip Planner helps the user select appropriate terrain based on the avalanche danger rating whereas Obvious Clues help the users “determine whether a slope is safe enough to cross ” (Haegeli & McCammon, 2006). The authors, the Canadian Avalanche Center (publisher of the Avaluator), Canadian avalanche educators, and the Canadian government all extol the Avaluator's simplicity as its main virtue and something that makes it superior to European decision support tools such as the 25-item Nivo test. A leading avalanche safety educator, Albi Sole, explained to the media: “I say keep it simple. Seven clues is plenty. ” and opined that the 25-item Nivo test is too complicated for most backcountry users, even though thousands of French have mastered its use. We examine this fixation on simplicity. First, we demonstrate the undesirable consequences of dumbing down curriculum in response to students ' preferences for simplicity and easiness. Second, using psy-chometric theory, we demonstrate that the Avaluator's Obvious Clues method is too simple to be reli-able, valid, and useful for making decisions about slope stability. Third, using Avalanche danger rat-ings and terrain classifications, we demonstrate that the Trip Planner is so simple that it recommends that users do not venture out most of the winter except perhaps on flat avalanche-free plains.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".