Development of a Problem-Solving Model from a Multi-Disciplinary PerspectiveDevelopment of a Problem-Solving Model from a Multi-Disciplinary PerspectiveDevelopment of a Problem-Solving Model from a Multi-Disciplinary Perspective
Bibliographic record
Abstract
Problem-solving is used every day, in almost every aspect of our normal lives. It’s not really thought about much, it is just done naturally. In the fields of engineering, education, medicine, philosophy, psychology, and business, experts will spend months, years, even lifetimes attempting to solve large and complex problems. This research looks not into solving these problems, but instead takes a step back and peers deeper into the process with which these problems are solved. Nvivo qualitative text analysis software was used to compare and contrast previous research that has been done into problem-solving across each of the disciplines mentioned above. Papers were separated manually into their various disciplines based on titles, journals, and keywords found in abstracts, in addition to being separated by decade. The papers were then analyzed using this software in order to find information such as word frequency in order to compare common themes and wording found within previous research done on ‘problem-solving' for each discipline. These results were analyzed in order to make qualitative conclusions, such as noting key similarities and differences between the disciplines of interest. It is hoped that this research may be used in the future to further compare how various disciplines go about the process of problem-solving, in order to ultimately optimize how problem-solving is done across all disciplines. Department: Engineering Faculty Mentor: Dr. Jeffrey Davis
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".