Bibliographic record
Abstract
The goal of this study is first to provide superior predictions of Czech GDP growth to the o cial estimates of the Czech Statistical O ce and the proxy estimation of the Czech National Bank. Secondly, to expand the literature that focuses on machine-learning predictions that utilizes data with various sampling frequency. Although in the first goal, this thesis did not succeed as all models, namely Ridge and Random Forest, failed to beat the predictions of o cial institutes, the thesis contributes to the yet scarce literature on mixed-frequency machine-learning prediction. Since no machine-learning model accounts for data with various frequencies, the thesis shows how to transform variables so that any machine-learning model can utilize them. Furthermore, di erent dataset modifications are explored, such as the prediction time: end of the reference quarter (nowcast) and 40 days after the reference quarter (backcast), standardized and non-standardized datasets. And finally, for the superior Ridge model, the e ect of so-called high-frequency variables (sampled every week) is explored. While Random Forest showed little e ect by using di erent versions of the dataset, in the case of the Ridge model, the type of dataset had a significant e ect. While the non-standardized Ridge produces better overall...
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".