Exploring the Level of Meeting the Economic, Environmental, and Social Sustainability of Southern Fertilizer Company According to GRI Standards: Evidence from Iraq
Bibliographic record
Abstract
In response to changes and challenges in the business environment, many global and local companies have sought to adopt sustainability due to stakeholder demands for sustainable practices in their operations.This study aims to explore the extent to which the general company for fertilizers industry meets economic, social, and environmental sustainability requirements, according to the latest edition of the GRI standards.To achieve this aim, a descriptive analytical methodology and a checklist were used; interviews were conducted with department and division heads to collect data and information relevant to the study.The checklist was designed in accordance with the GRI's economic, environmental, and social standards to determine the extent to which the study sample met these requirements.The results revealed that the level of meeting the economic sustainability requirements was 48%, environmental sustainability 33%, and social sustainability 61%.This study concludes that the fertilizer company prioritizes social sustainability over economic or environmental sustainability.This study contributes to bridging the research gap on this topic, as a review of previous local literature indicated the absence of a single study addressing this issue in the Iraqi manufacturing industries sector.Therefore, this study is the only one at the local level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".