Bibliographic record
Abstract
HOW CAN WE MEASURE THE SUSTAINABILITY?\nNagy Tamás\nWest-Hungarian University, Faculty of Economics\nIstván Széchenyi Management and Organisation Sciences Doctoral School\n\nThe science has a lot of developed indicators to measure and monitor economic, social phenomena and processes and environmental conditions, but we want now to measure sustainability. The first step is to make an exact definition of the sustainability. The definition made by the Brundtland Commission has two bases: the duality of needs and limitations. For this reason the growth of human needs can only be achieved with harmonic development. The harmonic development is based upon three grounds: on the social forms, on the economical level and on the carrying capacity of natural systems. Therefore, the economical efficiency determines the social reasonableness, and these are limited by the carrying capacity of natural systems. To reach this goal, I analyzed the mainstream of the science in this regard. The Canadian National Round Table on the Environment and the Economy made six sustainability indicators. A US company called Sustainable Measures defined ten measurable interaction factors of the sustainable development. The Global Report Initiative uses triple bottom lines in the assessment. In my dissertation, I survey and compare these methods, and make proposals for usable routines for the European companies.
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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 | 0.032 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.015 | 0.036 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".