Big data en gerelateerde begrippen gedefinieerd
Bibliographic record
Abstract
De hoeveelheid digitale data is de laatste jaren explosief gegroeid. De afgelopen twee jaar stond het thema ‘Big Data’ (en de daarmee samenhangende Big Data revolutie1) hoog op de agenda van de business en IT-wereld. Ook bij de beleidsmakers en de overheidsorganisaties heeft dit thema nadrukkelijk de aandacht. In de huidige digitale samenleving is de term ‘Big Data’ echter een ‘buzzword’ geworden, waarin ‘data-bergen’ (‘mountains of data’) het traditionele schaarse-data landschap lijken te overvleugelen. Deze data komen uit vele bronnen: GPS, GSM, camera’s, sensoren, websites, sociale media. De enorme groei van de hoeveelheid en diversiteit van digitale data, geeft organisaties steeds meer de mogelijkheid om bijvoorbeeld het gedrag van mensen en bedrijven te monitoren, te duiden en zelfs te voorspellen.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.018 |
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".