Bibliographic record
Abstract
Considering the tasks set by the government for the economy, the legal mechanisms for entering into contracts for the sale (supply) of goods on exchanges will continue evolving and improving, and the list of items that can be traded on exchanges will expand further. In order to create representative indicators and fair market prices for goods that are significant for the country (which cannot always be considered things), it seems reasonable to apply the concept of “goods” in a broader sense, including therein other objects of civil rights, other property, in addition to things, or even name specific property rights depending on which objects of civil rights the new “incorporeal” entities discussed above will be classified as in the Exchange Trading Law. An interesting question is that of listing on exchanges goods that are not things, but, as stated above, can also be traded on exchanges (carbon units, “green” certificates, and other similar objects). Considering the qualitative characteristics of such exchange-traded objects, one cannot speak of quality in the same sense as with things, nevertheless, the characteristics that objects may have in order to become exchange-traded commodities must be determined by the exchange establishing admission rules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".