INTERNATIONAL SYMPOSIUM THE CONTRIBUTION OF HUMAN AND SOCIAL CAPITAL TO SUSTAINED ECONOMIC GROWTH AND WELL-BEING AIMS OF THE WORKSHOP:
Bibliographic record
Abstract
• To bring together a number of different perspectives and disciplines in the analysis of the contribution of human and social capital to economic growth, productivity, social cohesion and human well-being; • To situate the important discussion of economic growth in a wider social context highlighting the various social antecedents of growth within which human and social capital play an important role as well as the broad “market ” and “non-market ” social returns to investment in lifelong learning; • To clarify important concepts and relationships in the area of human and social capital and assess the role of public authorities in areas such as learning, social programs and labour market responsibility; • Drawing on the work already carried out in the OECD on Human Capital Investment to identify possible new areas of research, data development and policy analysis; • To link the experience of Canadian researchers and policy-advisors in these areas to international comparisons and analysis. The Background One of the most striking economic phenomenon of recent years has been the sharp disparity between
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".