Bibliographic record
Abstract
Researchers often hold a romantic view of theory, which they feel should be a complete, flawless, deep, and exhaustive explanation of a phenomenon. They also often hold a romantic view of theory building, which they envision either as emerging from trancelike writing or as the product of a straightforward deductive process. The perspective I offer is more realistic and pragmatic. I espouse the view that the outcomes of a researcher’s theorizing efforts are often incomplete explanations of a phenomenon, which, given a chance, may develop into rich theories. I propose a highly iterative spiral model that portrays theory building as a craft, which calls for care and ingenuity, and requires patience and perseverance. I also propose design principles that can contribute to the quality of the outcome of theorizing.
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 | 0.110 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".