Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this chapter we present what is known about reasoning and problem solving, what is currently being done, and in what directions future conceptualizations, research, and practice are likely to proceed in the psychological literature. In our discussion, we attempt to clarify the distinction between reasoning and problem solving, present major theories of reasoning and problem solving, and emphasize the importance of background knowledge in both of these forms of thinking. In discussing the importance of knowledge in reasoning, for example, we offer evidence from studies of expert problem solving that illustrate the importance of background knowledge in facilitating the selection of strategies for successful performance. Moreover, we identify a recurring challenge for theories of reasoning and problem solving, namely, the frame problem (Dennett, 1990; Fodor, 1983). The frame problem involves deciding which beliefs from a multitude of different beliefs to consider when solving a task or when updating beliefs after an action has occurred. Finally, we conclude by suggesting that successful reasoning might be akin to expert problem solving—an iterative and systematic process of pattern detection and classification. Connectionist models can provide an avenue for investigating such a pattern classification account of reasoning.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 it