Upaya Kampanye Greenpeace dalam Internasionalisasi Isu Pencemaran Lingkungan di Tiongkok Pada Periode 2016-2018
Bibliographic record
Abstract
This study aims to analyze Greenpeace's campaign efforts or ways to internationalize the issue of environmental pollution in China. China is experiencing severe environmental pollution, where the air and water in China has been polluted due to industrial activities and excessive use of coal. In analyzing the issue, the writer uses neoliberalism and INGO theories. Because the theory is in line with the research questions the authors ask about INGO as a non-state actor. This type of research is qualitative research. The data used in this study are secondary data sourced from journals, books and international news sites about environmental pollution in China. The results of this study explain that Greenpeace in its campaign to internationalize the issue of environmental pollution in China held public discussion in Canada, used social media in campaigning, and attended UNFCCC conferences. So that the issue of environmental pollution in China can be raised and many international communities can find out the environmental pollution that occurs in China caused by too many factories and excessive use of coal.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".