120121Chapter 8 World Civility Index and Inner Development Goals: Connecting inner development to mainstream success
Bibliographic record
Abstract
This article introduces the World Civility Index (WCI), a pioneering initiative by the International Soft Skills Standards & Testing (IITTI) that integrates the principles of the Inner Development Goals (IDGs) framework, aiming to quantify and elevate the importance of soft skills in both the workplace and broader society. In an era where technical skills have traditionally dominated educational and corporate landscapes, the increasing significance of soft skills like empathy, communication, and intercultural awareness is becoming undeniable. Yet, formal recognition and measurable standards for these skills have been notably absent. The WCI emerges as a solution, offering a universal metric for evaluating and credentialing soft skills, akin to a personal credit rating in finance. Developed through a collaborative effort involving soft skills trainers and technology experts, the WCI promotes a micro-learning model, encouraging daily engagement with inner development qualities and providing tangible proof of personal growth. This novel approach not only enhances employability and company culture but also aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 8 emphasizing decent jobs and economic growth. Deployed across 19 countries and embraced by diverse organizations, the WCI represents a significant step toward integrating essential human values into global business practices, thereby fostering a more civil, empathetic, and interconnected 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.104 | 0.024 |
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".