Обратная связь с предприятиями-участниками мониторинга НБРК: проблемы и пути совершенствования // Feedback with the NBK survey participants: problems and ways of improvement
Bibliographic record
Abstract
Учитывая добровольность участия предприятий в мониторинге Национального Банка Республики Казахстан, поддержание уровня репрезентативности, необходимого для адекватного отражения изменений экономической конъюнктуры, является нелёгкой задачей, особенно для территориальных филиалов, ответственных за сбор данных к установленным срокам. Начиная с 4 квартала 2015 года, наблюдается снижение количества крупных и средних предприятий в выборке, что, в свою очередь, отразилось негативно на репрезентативности выборки. Единственным способом удержания предприятий, в условиях добровольности, является направление каждому предприятию-участнику мониторинга Возвратной Информации (ВИ). Именно ей посвящена данная статья. В статье описывается содержание направляемой предприятиям ВИ, и рассматриваются возможности ее совершенствования на примере одного предприятия. // Given the voluntary participation of enterprises in the monitoring of the National Bank of the Republic of Kazakhstan, maintaining the level of representativeness necessary to adequately reflect changes in the economic environment is not an easy task, especially for regional branches responsible for collecting data by the established deadlines. Starting from the 4th quarter of 2015, there has been a decrease in the number of large and medium-sized enterprises in the sample, which, in turn, had a negative impact on the representativeness of the sample. The only way to retain enterprises, in terms of voluntariness, is to send Return Information (RI) to each participating enterprise. This article is dedicated to her. The article describes the content of the RI sent to enterprises, and discusses the possibilities of its improvement on the example of one enterprise.
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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".