Анализирање на зависноста помеѓу бруто - домашниот производ и трговијата на мало во Република Македонија во период од 1990 до 2009 година со примена на економетриски модели
Bibliographic record
Abstract
Gross Domestic Product - GDP is the the widest measure of economic activity. Annual quarterly percent changes in GDP refl ect the growth rate of overall economic results.The fi gures can be quite volatile from quarter to quarter. Inventory and net export swings in particular can produce signifi cant volatility in GDP. The fi nal sales fi gure, which eliminate inventories, can sometimes be useful in identifying underlying growth trends as inventories represent unsold goods, and a large inventory increase will boost GDP but might be indicative of weakness rather than strength. The monetary value of all services and fi nal goods produced within a country’s borders in a particular time period, though GDP is usually calculated on an annual basis. It includes all of private and public consumption, government outlays, investments and exports less imports that occur within a defi ned territory. GDP is totally comprehensive and detailed report. Actually, reading the report brings us back to many of the indicators. GDP includes many of them: retail sales, personal consumption and wholesale inventories are all used to help calculate the gross domestic product.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".