Banco de España Business Activity Survey: 2024 Q4
Bibliographic record
Abstract
Rationale The Banco de España Business Activity Survey (EBAE) provides highly valuable, real-time information on a broad sample of Spanish firms’ turnover, employment, costs and prices. This helps to diagnose current economic developments. Takeaways •Firms perceive an increase in turnover in 2024 Q4, compared with the decreases in the same quarter of 2022 and 2023. Those located in the Valencia region have reported adverse turnover developments as a result of the impact of the flash floods. •Inflationary pressures have ticked up slightly in Q4, in both the cost of inputs and selling prices. •The percentage of firms affected by higher borrowing costs and insufficient demand has fallen, but economic policy uncertainty and labour shortages remain high. •In a module on advanced technologies, 3% of respondent firms reported a high or moderate use of artificial intelligence (AI) systems and 11% are experimenting with them. For firms not yet using AI, the main barriers are a lack of skilled staff, prohibitive costs and significant uncertainty about the legal ramifications.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".