Unpacking the process of conceptual leaping in the conduct of literature reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Literature reviews serve diverse purposes, including description, understanding, explanation, and testing. Traditionally – before online databases, full-text search availability, and AI-based search tools – identifying relevant sources might have been considered a valuable contribution. However, top-tier information systems (IS) journals now demand more than descriptive reviews; they require authors to move beyond summarizing existing knowledge toward proposing innovative research directions, important research questions, new concepts, and interesting linkages among concepts. Despite adhering to rigorous methodological guidelines, many authors struggle to make conceptual leaps, that is, to elevate their literature reviews beyond description, to achieve a profound understanding, to provide explanations, or to develop a model. Authors may mistakenly prioritize hard work – like thorough literature search, analysis, and organization – over hard thinking, which is crucial for advancing theoretical contributions. With this in mind, I adopt the view that the literature is indeed qualitative data. I suggest that approaches that help make conceptual leaps in qualitative research can benefit literature review authors searching for inconsistencies in the extant literature and developing new questions, concepts, and linkages. Drawing upon qualitative research (Klag, M., and Langley, A., 2013. Approaching the conceptual leap in qualitative research. International Journal of Management Reviews. 15 (2), 149–166.), I unpack the process of conceptual leaping in the conduct of literature reviews. This process involves navigating dialectic tensions between knowing and not knowing, engagement and detachment, deliberation and serendipity, and self-expression and social connection. Effectively managing these tensions can help authors increase the impact and innovativeness of their literature reviews.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it