Scraped data as a source to study the demand for ICT specialists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it