Bibliographic record
Abstract
In the movie The Gold Rush Charlie Chaplin and his friend are stranded in a log cabin in the middle of winter while a blizzard rages. The cabin is isolated, and they have a very big problem – there is nothing to eat. They pace around wondering what to do. Charlie's friend starts to see Charlie as a chicken, and he tries to kill him. He chases Charlie around the cabin many times. Eventually they hit upon the idea of boiling an old boot and eating it for dinner. With great delicacy they sit at the table and eat the boot as if it were a gourmet meal. They solved the problem of having nothing to eat. While their solution to the problem did not result in a culinary feast, this example reveals two crucial features of problem solving. First, a problem exists when a goal must be achieved and the solution is not immediately obvious. Second, problem solving often involves attempting different ways to solve the problem. Put more formally, a problem has four components. First, there is an initial state. This is the person's state of knowledge at the start of a problem. Second, there is the goal state: the goal that the person wishes to achieve. Third are the actions or operations that the problem-solver can use to get to the goal state. Fourth is the task environment that the solver is working in. The task environment consists of the features of the physical environment that can either directly or indirectly constrain or suggest different ways of solving a problem. I will sketch out the main currents of thinking in research in this area, beginning by reviewing the history of research on problem solving and then focusing on a number of important issues in problem-solving research. Finally, I will give an overview of some recent developments.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.028 |
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".