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.
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 | 0.101 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".