Reporting on Long-Term Value Creation—The Example of Public Canadian Energy and Mining Companies
Bibliographic record
Abstract
This study empirically analyzes reporting on long-term value creation for Canadian public mining and energy companies. It represents an important first step in determining the quality of reporting and its determinants for large public companies. In this exploratory empirical study, a reporting quality scoring index was developed to measure disclosure quality of long-term value creation reporting. Content analysis was used to examine financial and sustainability reports for a sample of twenty Canadian public mining and energy companies. Corporate disclosure quality scores were then calculated by assessing the quality of reporting in four main categories. The findings suggest that overall disclosure quality on long-term value creation is still low. Companies disclosing higher quality information on long-term value creation are of bigger size, operate in the basic materials sector, have an independent board, are listed in the Dow Jones Sustainability Index, experience higher stock volatility and use more words related to long-term value creation in their annual financial and non-financial reports. In order to increase and restore stakeholder trust and credibility as well as a tool to maintain stability, it is strongly recommended to introduce adequate mandatory standardization resulting in a set of internationally recognized reporting standards as well as a requirement for external assurance of 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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".