Scraped data as a source to study the demand for ICT specialists
Bibliographic record
Abstract
The ongoing digitalisation is profoundly transforming society, businesses, and the economy at large. Its impact also extends to official statistics, challenging traditional approaches to data collection and analysis. Statistical systems are embracing this shift by advancing along two key lines of work: the adoption of emerging technologies, and the utilisation of new data streams generated by the increasing datafication of our societies. One of the earliest use cases of this approach was the analysis of Online Job Advertisements. From the initial contextual analysis of job portals through web-scraping and data transformation into structured, coded formats, the complex methodology developed is continuously validated to ensure the production of high-quality statistics. The first results disseminated by Eurostat were focused on ICT specialists. According to the indicators used, ICT specialists accounted for 7% of total job advertisements in Europe during the first quarter of 2025, with Luxembourg (18.3%) and Malta (15.4%) leading. A key strength of this data source lies in its granularity, offering insights at the NUTS2 regional level, where statistical information is often limited. As a new statistical source, Online Job Advertisements also face exciting challenges that will shape not only the future of OJA-based information but also the evolution of next-generation statistics.
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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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".