The Information Cost Estimation as Realization of the Problem of Indistinct Mathematical Programming
Bibliographic record
Abstract
All information in the market has the consumer value. It is possible to estimate the recommended cost of investments in chance of reception of exact future result on the basis of calculation of consumer cost of the full information, and it is defined as a difference between expected values of a choice at presence or absence of the full information.The problem of definition of cost of the information a priori is connected with necessity of the account of variety of the uncertain factors characterizing, firstly, its reliability, and, secondly its utility. It is possible to estimate reliability or utility of any information on the basis of the available statistical data, or expertly. Certainly, statistical methods allow to receive plausible enough estimations, however, by no means always the researcher possesses necessary statistical base that speaks or absence of the admission to it for privacy reasons, or exclusiveness of the information. The formalization of a problem of definition of information costing as а problem of indistinct mathematical programming, proceeding from purposes of the person, making decision (PMD), and on the basis of processing of the expert data is carried out in given work.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".