Linkage between strategy and financial performance disclosure in annual reports: A new reporting path for organizational learning
Bibliographic record
Abstract
We examine the reporting practice of linkage between strategy disclosures (management discussion of firm strategy and the business model) and financial performance disclosures in annual reports as a path for organizational learning. For identification, we use the UK Company Law Amendment mandating a Strategic Report as a separate section of the annual report so that strategy disclosures provide sufficient context for financial statements. We confirm that the linkage between strategy and performance disclosures in annual reports increases incrementally after the amendment for firms that are more strongly affected by the Company Law Amendment. For these firms, we document an incremental rise in measures associated with organizational learning (e.g., measures of organizational changes and workforce engagement). Our study has important policy implications for the structure of textual disclosure in annual reports.
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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 | 0.054 | 0.358 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".