The Relationship between the Needs changes of Public or Personal and Rational Expanding of Government Scale
Bibliographic record
Abstract
The law of the needing levels discovered by Maslow is a famous theory, but the theory mainly works in private domain. In this paper, the Maslow's law is deduced into public domain, putting on the idea of public Maslow's phenomena and the public maslow's law. By methods of theoretical analysis blended with positive analysis, quality analysis blended with quantity analysis, the ideas presented above have been tested and verified. In the end of the paper, it points out that public Maslow' s law is an important cause for the expanding of public department as well as for the rational expanding of government scale. Keywords: The Maslow's law, public realm, the scale of government Resume: La loi du niveaux de besoins decouverts par Maslow est une fameuse theorie, mais cette theorie fonctionne principalement dans les domaines prives. Dans ce document present, la loi de Maslow est deduite dans le domaine public, en mattant les idees du phenomene public de Maslow et de sa loi publique. A travers les methodes theoriques d’analyse melangee avec l’analyse positive, analyse qualitative melangee avec l’analyse quantitative, les idees exprimees dans le precedant etaient testees et verifiees. A la fin de ce document, il designe que la loi publique de Maslow est une explication importante pour l’expansion du department public a la fois pour l’expansion rationelle des forces du government. Mots-cles: loi de Maslow, realm public, les forces du government
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".