Bibliographic record
Abstract
Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. Various organizations, such as those active in business or in politics, capture, store, and analyze data gathered from heterogeneous, distributed, and sometimes autonomous information sources including social media, classical and Web databases, multimedia files, localization raw data, etc. Big data is a term used to describe such kind of data having high volume, velocity, and variety features. It involves innovative technologies, such as pervasive and cloud computing ones, used to enhance decision making, provide vision, discovery, and backing to improve business processes and actions on the ground. The latent value of big data analytics is prodigious and clearly recognized by a rising number of studies such as topics related to politics. The tutorial will focus, in general, on big data and related analytics rising topics, and, in particular, on big data studies performed in the modern democracy era. Modeling and analysis approaches will be discussed, and various cases of election big data, including experiences performed in old democracies such in the United States, Canada, and the new ones such in Tunisia, will be presented.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".