Exploring How the Balanced Scorecard Engages and Unfolds: Articulating the Visual Power of Accounting Inscriptions
Bibliographic record
Abstract
Abstract Research on the Balanced Scorecard (BSC) has questioned whether its adoption generates greater integration between financial and nonfinancial performance measures, supports strategy implementation, increases performance and improves strategic decision making. This research has also highlighted how theBSCoften generates effectsotherthan these. Building on studies that portray Performance Measurement Systems as intrinsically incomplete practices of representation, the purpose of this article is to investigate how theBSCengages users because of the organizing work that it stimulates around this incompleteness. Our findings allow us to further articulate the power of specific visual elements of theBSC, such as hierarchical trees, wheels, causal and strategy maps. The article provides material that contributes to a better understanding of how theBSCperforms multiple roles within organizations beyond a simple representational functionality and unfolds continuously. It contributes to the growing literature on accounting inscriptions, that is, signs producing incomplete representations, by developing an analytical theoretical framework that defines theBSCas a rhetorical machine composed of four key features: (i) a visual performable space (i.e., a schema generating creative engagement); (ii) a method of ordering and innovation; (iii) a means of interrogation and mediation; and (iv) a motivating ritual.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".