Adaptive methodology. Topic, theory, method and data in ongoing conversation
Bibliographic record
Abstract
This paper explores the concept of adaptive research design, in which topic, theoretical framing, method, and data are in principle open to adaptation during the research process. The main premise is that adaptations in one element of the research process can trigger changes in other elements. Both positive and negative reasons for adaptivity are discussed along with various valid reasons for limiting adaptivity in particular cases. Grasping the different couplings between concepts, theories and methods is useful to discern the possibilities and limits of adaptive methodology in situ. To deepen the understanding of the adaptive capacity of methodology, we broaden the discussion to look at the embedding of methodology in academia and its disciplines. In our perspective, methods appear as devices structuring thinking and observation and are well used and placed if they enhance and enable the continuation of observation and reflection and if they allow the researcher to remain open for alternative observations and interpretations.
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 | 0.144 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".