The use of indicators in Canadian corporate sustainability reports
Bibliographic record
Abstract
The purpose of this thesis is to explore the use of sustainability indicators in Canadian corporate sustainability reports. The literature review highlights that few details are available on how indicators are currently used by corporations. To address this gap, this research focues on a content analysis of sustainability reports published by Canadian corporations in 2008. This thesis provides the first comprehensive review of indicators used in Canadian corporate sustainability reporting. Thematic categories of indicators, their use by industry sector and their associated targets are discussed. The use of existing sustainability indicators programs, such as composite indices, the GRI and Balanaced Scorecard, is also presented. The GRI indicators selected by Canadian corporations are also reviewed in detail. Finally, the way corporations report on the selection, development, and use of indicators int the management of sustainability issues is analysed.
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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 | 0.032 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.039 | 0.082 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".