The Method of Problems versus the Method of Topics
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 Confused students researching papers not knowing where they are going. Articles, lectures, and books on exciting topics that turn out to be boring. Such familiar phenomena are symptoms of a widespread, largely unconscious methodological habit of focusing on topics rather than problems. This habit rests on views about knowledge that are deeply ingrained in commonsense knowledge and in the methodology of mainstream social science. Such views saturate the understanding of scientific inquiry assumed by most methods textbooks. This article criticizes the method of topics and contrasts it with the method of problems. The word “topic” suggests that there is some surface to cover, but not why covering it might be interesting. Interesting research is problem-driven. It begins with a sense that something is amiss with existing knowledge and requires explanation. Problem-driven research begins, not with collection of data or facts, or with clarification of concepts, but with identification of inconsistencies or gaps in existing knowledge. It seeks to solve problems through free invention and severe criticism of hypotheses.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it