Verification of the typical ratio between both the number of households and business entities (quantum satis) for selected countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a G = αX ratio between the number of households (G) and the number of business entities (X), where α equals 5.00 for well-developed countries. The aim of the study was to verify this proportion (called ”quantum satis”) for countries with significant number of population in the period of 2010—2016. It involved countries such as Brazil, Canada, China, India, Poland, Russia and the USA on the basis of data obtained from the statistical offices websites and the OECD. The formulas of the regression function were used in the research. In 2016 such proportion was reached by Russia and China. For the economies of highly developed countries the value of α may be less than 5,00, which is influenced by processes such as outsourcing, entrepreneurship and economic liberalism (such countries include i.a. the USA and Canada).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it