Beyond Disclosure: A Comprehensive Scoring Scale for Evaluating Integrated Reporting Quality
Bibliographic record
Abstract
ABSTRACT This research article aims to develop a structured and comprehensive scale for integrated report quality assessment. In fact, it fills the gap in the literature by providing a valid scorecard system through which the quality of integrated reports is measured against the predefined constructs of the IIRC framework and other conceptual factors. The methodology, informed by the scale development process proposed by Churchill, therefore adopts a mixed‐methods approach through literature reviews, exploratory factor analysis, and confirmatory factor analysis. Evidence from 181 and 307 respondents in the United Kingdom, Canada, and India attests to the reliability and use of this scale. The final scale consists of four dimensions that measure: (1) IR content elements, (2) fundamental IR principles, (3) guiding principles, and (4) other determinants of report quality. The results emphasize transparency, strategic focus, and stakeholder engagement as central characteristics of good integrated reporting. The scale is a systematic tool for regulators, managers, and investors to improve report quality, enhance transparency, and support informed decision‐making in corporate governance, integrated, and sustainability 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 | 0.047 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".