Social business, accountability, and performance reporting
Bibliographic record
Abstract
Purpose The purpose of this paper is to contribute to the theory and applications in social business and accountability. Design/methodology/approach The paper develops the theoretical arguments, shows the importance of non‐accounting measures, explores available non‐accounting measures and suggests BSC as an externally validated reporting tool. Findings There is a need to expand the accounting base to non‐financial measures; social business and social enterprises do not have externally validated performance reports and there is no benchmark data to compare performance. Research limitations/implications This is a conceptual and theoretical study. It needs empirical validation. Practical implications Using the suggested measurement and reporting will make public accountability transparent and expand the accountant's social role. It will motivate teaching of social business in accounting. Social implications The study supports social business as a legitimate entity; corporations engaged in social business will be more publicly responsible; the study will encourage investment in social business; small entrepreneurs from the bottom of the society will have an opportunity to participate in the economy; and the poor will participate in the economy, will expand the economy and contribute to social and economic development. Originality/value The paper includes guidelines for implementing the proposed BSC, performance measurements and reporting techniques.
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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 | 0.046 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".