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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".