Sustainability Reporting Pattern in Pharmaceutical Sector : A Study of Top 10 Economies across the Globe
Bibliographic record
Abstract
Sustainability reporting is now a mainstream activity of global corporations and is an important issue of the decade. Transparency and accountability for stakeholders are the most demanding issues in pharmaceutical sectors. Companies or Industries can’t survive without sustainable growth. Since most of the stakeholders are aware of recent problems such as community health, climate change, education and development, business sustainability, etc., the demand for disclosures in these areas have also been remarkably increased. Global companies have started business sustainability for economic and non-economic activities of the venture, along with the accountability of external and internal stakeholders towards the goal of sustainable development. This paper examines the sustainability reporting practices of the top 10 economy's pharmaceutical companies across the globe. For this purpose, sustainability reports based on GRI and Non- GRI guidelines for 5 years (2012 to 2016) of the top 10 economy's pharmaceutical companies were collected from the GRI-Database. The number of pharmaceutical companies along with a country name that published sustainability reports has been classified into four categories such as companies with GRI reports are published for 5 years, less than 5 years, Non-GRI reports and mixed reports (GRI &Non-GRI) and a total number of reports published in the given time periods. The results revealed that the sustainability disclosures in Pharmaceutical sectors are dominated by both the 1st and 2nd largest economies across the globe USA, China, and Brazil, and the worst sustainability disclosures are Canada, Italy, Germany, and India. It means pharmaceutical companies in the USA, China, and Brazil are more conscious about sustainability reporting as compare to the rest of the countries of the top 10 economies in the world.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".