Stepanenko. Digital Market Development Prospects: Huang Law vs Moore's Law
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
Работа посвящена исследованию влияния информационных технологий на эффективность бизнес-процессов компаний. Выявлены ключевые факторы успеха компаний в условиях цифровизации. Определены закономерности успешного развития компаний Intel и Nvidia, проведена сравнительная характеристика закона Мура и закона Хуанга. Сделаны выводы об актуальности действия законов в зависимости от сферы применения и приоритетности экономических показателей оценки эффективности деятельности компании. We study the impact of information technology on the efficiency of companies' business processes. We reveal key factors for the success of companies under digitalization. Patterns of successful development of Intel and Nvidia are determined; the comparative characteristic of Moore's law and Huang's law is given. Conclusions are made about the relevance of the laws depending on the scope and priority of economic indicators for assessing company performance.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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