Perspectives On Hydraulic Fracturing In Canadian Shale Gas Plays (a Business Case For Effective Corporate Sustainability Strategy)
Bibliographic record
Abstract
In recent times, the challenging issues of climate change and global warming have increased the environmental consciousness of governments, firms, industry, non-governmental groups and the general public on a global scale (Banerjee S.B., 2001). Governments and industry are exploring innovative ways to address the increasing pressure while responding to the rapid increase in the demand for energy and economic growth. “Governmental monitoring and control of environmental impacts of business activity is a process that is designed to minimize the negative consequences of environmental damage” (Banerjee S.B., 2001). Corporate strategy can be used as a brilliant tool in bridging the widening gap between environmental pressures and the vision of an organization. This blending of vision with an environmental lens is a highly complex task. “Understanding managerial perceptions of environmental issues provides an effective framework for strategic bridging” (Westley & Vredenburg, 1991). This will also aid in promoting collaboration between businesses and environmental or governmental agencies. This research highlights the highly controversial energy issue of hydraulic fracturing in Canadian shale gas plays as a case study for effective corporate sustainability strategy. Corporate Strategy can be referred to as the brain child behind corporate success. It works more like the manual that accompanies an equipment or appliance, or the technique required to achieve a goal. It is not about doing everything; it is about prioritizing effective responses to societal concerns in daily operations. It is about doing the most important and most effective things at the right time. It is more than the glossy pages of sustainability reports and great websites.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.018 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".