Bibliographic record
Abstract
The fundamental point of this article is that methods change what we are able to see. Using a series of examples, the article suggests that substantive literatures are regularly set off course by the limitations built into widely used methodological frameworks to analyse data. In each example, simpler and/or better known methods are compared to methods which either incorporate a broader range of possible influences or clarify the issue at hand, and consequently essential findings change. These findings suggest either that the inertial state of findings in research literatures could be redefined or redirected by the application of methods which take into account more clearly the effects of time and place, or that given theories may be transformed by methods which resolve conflicting or limiting features of theoretical debate. Examples cover a wide range of issues, from longitudinal vs cross-sectional data, to the specification of models to replace equations, to the influence of social context on individual behaviour, to the importance of time in the modelling of events, to the nuances of capturing the complexity behind interdependent processes over the life course.
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 | 0.341 | 0.633 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.031 | 0.048 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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; the direct Gemma label and the distilled Codex classifier 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".