Computer science and geodemography: data matching and anomaly detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it