A vision on future advanced data collection
Bibliographic record
Abstract
Society’s demand for data-driven, fact-based information continues to increase. National statistical offices play a critical role in providing this demand-driven information to support evidence-based policy making. Thereby transforming from suppliers of official statistics to providers of trusted smart statistics. The digital transformation, data revolution and emergence of “big data” all influence the way NSOs collect data. Data are everywhere, generated by everything and everyone being stored in numerous locations and devices. The nature of data collection is bound to change. Using solely primary data collection would be too time-consuming, costly and burdensome to satisfy the increasing demand. NSOs should aim to use the vast amounts of data available in our digital society to be used as inputs for new statistical products, to supplement existing data acquisition or as replacements for existing survey inputs. Many areas must be taken into account including new data sources, collection methods and collection process redesigns. This comes with consequences with respect to methodology, technology, quality, metadata and standards, confidentiality, privacy etc. Knowledge development requires collaboration between NSOs, governments, end users, academic institutions, research organizations and private sector companies. Social acceptability needs to increase to maximize the benefit of these data sources to produce smart 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.
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 | 0.180 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.024 | 0.051 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".