Bibliographic record
Abstract
This paper reviews the literature on an important but mysterious phenomenon in qualitative research methodology: the conceptual leap that generates abstract theoretical ideas from empirical data. Drawing on epistemological, prescriptive and reflexive writings, conceptual leaps are described as constituted by both ‘seeing’ and ‘articulating’, as grounded in abductive reasoning, and as part of an ongoing dialectical process. Methods for approaching conceptual leaps and the conditions for their realization are discussed in the context of four dialectic tensions: between deliberation and serendipity, between engagement and detachment, between knowing and not knowing, and between self‐expression and social connection. The literature review suggests that conceptual leaping is best portrayed as a form of bricolage, drawing resources from the different poles of the four dialectics. Moreover, written and verbal communication play important roles in enabling synthesis. The paper concludes by calling for greater openness and legitimacy for reflexive accounts, as well as further research into the process of discovery in qualitative research.
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 | 0.530 | 0.498 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.018 | 0.111 |
| Scholarly communication | 0.029 | 0.049 |
| Open science | 0.010 | 0.035 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".