Theoretical Research on the Principle of Equality in Civil Law from the Perspective of Civil Society
Bibliographic record
Abstract
The assertion of equality of rights and abilities in the equality principle of civil law is incompatible with social practice. This kind of realistic contradiction forces the study of the essence of the equality principle of civil law to change a new path from “what is the principle of equality of civil law” to “what is not”. Based on this, the “essence” of the equality principle of civil law is to highlight the “principle” attribute of “equality” by excluding regularity assertion, and guarantee the qualification equality of social subjects in the field of private law by expanding rather than limiting the boundaries of interpretation space. The “essential” argument of the equality principle of civil law makes the interpretation of negative and positive implications logically possible. Civil law is based on the principle of equality. The negative implication of the equality principle derives the principle of freedom. The positive implication gives rise to the principle of honesty and credit and the principle of fairness and justice, which are the guarantees of the principle of freedom. Civil law establishes communication with the times through the dynamic and synchronic nature of the binary meaning of the equality principle，and equalizes the governance of civil law through the negative and positive implications of "one goes up and the other goes down".
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".