Bibliographic record
Abstract
Abstract A problem is a state of difficulty that needs to be resolved. For example, if you accidentally lock your car keys inside the car, the problem is how to get home or wherever you need to go without access to the car. Problem solving is the goal‐driven process of changing one state of difficulty into a state that does not include the source of difficulty (Simon, 1999). The state without the source of difficulty is the desirable state. According to Sternberg and colleagues (e.g., Pretz, Naples, & Sternberg, 2003, pp. 4–5) and others (Bransford & Stein, 1993; Hayes, 1989), the problem‐solving process can be described as a cycle of seven steps or events: (1) a problem is recognized or identified in the environment; (2) the problem is defined and represented mentally; (3) within the mental representation generated, a solution strategy is developed to solve the problem; (4) relevant knowledge about the problem is organized; (5) the physical and mental resources needed to solve the problem are distributed; (6) progress toward the goal of solving the problem is monitored; and (7) the solution is evaluated for meeting the goal of solving the problem.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.104 | 0.035 |
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".