Developing Novel Technique for Investigating Guidelines and Frameworks: A Text Mining Comparison between International and Japanese Green Bonds
Bibliographic record
Abstract
In most cases, the official documents related to guidelines and frameworks are complicated, long, and hard to understand for general readers, regardless of whether the government and financial companies follow international standards or not. In this context, the current study examines how the green bond (GB) guidelines created by the Japanese government are aligned with the Green Bond Principles (GBP) and Climate Bonds Standard (CBS) through a text mining technique. It also investigates whether the GB frameworks for the Japanese public and private companies follow the GB guidelines of the Japanese government. While the CBS is the guideline that focuses on climate bonds, the GBP specializes in GB whose scope is broader. The word frequency and word cloud analyses identify that the documents created by the Japanese government and companies have more similarities with the GBP, indicating that the Japanese GB guidelines and frameworks are more aligned with the GBP than the CBS. A pairwise word network matrix analysis also reveals that the Japanese GB guidelines and frameworks are more focused on broader environmental issues and sustainability than the CBS, which had more similarities with the GBP than the CBS.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".