Bibliographic record
Abstract
Purpose This paper aims to focus on the role of graphics in the propagation of the Balanced Scorecard (BSC) through the persuasive capacity of graphism to “scientize” management ideas. Scientization, through professionalization of knowledge, rationalization of management and the empowerment of human actorhood, is widely seen as an important element in embedding new management concepts and techniques; a determination based on some version of the positivist belief that science offers a privileged access to reality. Design/methodology/approach Based on an analysis of popular literature of the BSC in core business media during 1992 and 2010, the paper focuses on the publications authored by Kaplan and Norton, the creators and authority on this topic. Findings The paper argues that the use of graphics has played an important role in promoting the claims made by proponents of the BSC by portraying the technique as both scientific and as descended from a venerable tradition of knowledge. Specifically, it argues that graphics are mobilized to: enable the technique to be portrayed as developing cumulatively towards the present vantage, from flawed measurement to management break‐through; promoters of the BSC to defensibly extend claims about the BSC (i.e. rationalize management through the visual representation of causality and strategic focus); and open up multiple interpretations and iterations of concepts which enable the empowerment of human actorhood (i.e. management). Originality/value This paper contributes to the accounting literature relating to diffusion of management innovations, and research examining the generative mechanisms and the processes through which management innovations come about.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".