Bibliographic record
Abstract
Purpose The purpose of this paper is to provide guidance for designing and generating cumulative knowledge based on qualitative research. Design/methodology/approach The paper draws on the philosophy of science and specific examples of qualitative studies in accounting that have claimed a cumulative contribution to knowledge to develop a taxonomy of theoretically justified approaches to generating cumulative knowledge from qualitative research. Findings The paper argues for a definition of cumulative knowledge that is inclusive of anti-realist research, i.e. knowledge is cumulative if it increases the extent and density of intertextual linkages in a field. It identifies the possibility of cumulative qualitative research based on extensions to the scope of the knowledge and the depth of the knowledge. Extensions to the scope of the knowledge may include expanding the time periods, context, and/or theoretical perspective used to explore a phenomenon. Extensions to the depth of the knowledge may include new empirical knowledge, methodological pluralism, theory elaboration, or analytic generalization. Individual studies can demonstrate their contribution to cumulative knowledge by locating their research within a typology/taxonomy that makes explicit the relationship of current research to past, and potential, research. Research limitations/implications The taxonomy may be useful to qualitative researchers designing and reporting research that will have impact on the literature. Social implications The increased use of research impact as an evaluation metric has the potential to handicap the development qualitative research which is often thought of as generating non-cumulative knowledge. The taxonomy and the strategies for establishing cumulative impact may provide a means for this approach to research to establish its importance as a contribution to knowledge. Originality/value The concept of cumulative knowledge has not been systematically applied to research based on qualitative methods.
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 | 0.062 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".