Bibliographic record
Abstract
Governments are continuously taking initiatives to look into international education opportunities and outcomes of developed policies and projects. These policies are implemented to enhance the social and economic visions of individuals, provide greater efficiency for primary education and to meet the educational demands of rising population. The Organisation for Economic Co-operation and Development (OECD) Directorate for Education and Skills contributes to global education by creating new policies and quantifying the indicators of education. In addition to education, human resource development (HRD) is equally important to achieve sustainability. All indicators of education and HRD assist government in effective implementation of new projects or policies for sustainable development. Education and HRD sustainability requires a range of users from government organizations to academicians to the general public to analyze the collected data for implementation of policy within the education system of the nation as well as in the corporate sector. Therefore, the chapter focuses in detail on education and HRD systems and their role in sustainability. Objectives and potential improvements in education to achieve global sustainability are clearly defined in the subsections. The interdependency between education and HRD is also highlighted with a focus on interdependencies of sustainability on HRD by analyzing the HRD indicators for sustainability.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".