Bibliographic record
Abstract
Purpose The purpose of this paper is to contribute to the polyphonic debate on the future of interpretive accounting research (IAR) by addressing the issues of cumulative knowledge and embedment of IAR in wider literatures. Design/methodology/approach McCracken's method of inquiry, adapted to incorporate meso‐level considerations, can be used to help resolve these issues. Accounting‐related phenomena can be studied by first identifying the cumulative knowledge contributed by different theoretical perspectives that provides broad skeletal categories to be investigated in the context of an interpretive study. In addition, micro‐ and macro‐level “external” theories are incorporated in a global meso‐level framework to provide a high‐level lens to guide data generation and analysis, fostering the embedment of IAR in wider literatures. Findings Meso‐level research implies thinking organizationally and behaviourally, and thinking about linkage. By extension, it requires reflecting on the characteristics of the context in which the phenomenon occurs and the actors behave, the nature of the task or decision to perform, and possible links between macro‐ and micro‐factors that help identify “external” theories and frameworks that contribute to understanding the phenomenon. Research limitations/implications The contribution of the suggested approach is highlighted in the context of financial accounting research. Reflexive accounts on the choice and use of a meso‐level approach are presented, and the issue of appropriate balance between theoretical and empirical material is addressed. Originality/value Creativity is fostered when cumulative knowledge about a specific phenomenon is embedded in wider meso‐level theoretical perspectives, leading to the discovery of new insights about the topic under study and contributing to the advancement of knowledge.
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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 | 0.229 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.006 | 0.078 |
| Scholarly communication | 0.033 | 0.049 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".