Computer science and geodemography: data matching and anomaly detection
Bibliographic record
Abstract
For the first time, digital inequality was discovered on the example of using a new measure of information theory for socio-physical systems and LCLB calculus in relation to regions/countries/continents: New Zealand, Canada, Africa, South and North America, Australia. The identification of digital inequality was carried out on the basis of a study of open IT-platform statistics on the development of the COVID-19 pandemic in these regions/countries/continents. A conditional uncalibrated amount of information showed the best conditions for achieving favorable goals (for a person) by the "society-human-virus" system in New Zealand and in some African countries (where there is an irrationally productive way of making decisions). We draw attention to the fact that the "society-human-virus" system behaves as a single information/computing/computer system (in other words, as a sociotechnical system). Thus, the LCLB calculus used in this work can be effectively applied in pandemic computer science, as well as for highly effective forecasting of the socio-political situation in real time.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".