Big data en gerelateerde begrippen gedefinieerd
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
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.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.018 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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