Navigating the value of Big Data through an intricate scientific landscape
Bibliographic record
Abstract
The intricate nature of Big Data within scientific research, combined with the persistent debate surrounding its definition, makes it challenging to offer a rigorous and objective assessment of its value to scientific inquiry. The aim of this dissertation is to navigate the complex landscape of Big Data discourses and practices, offering a critical and comprehensive perspective on how Big Data can be valued within the scientific research realm. This study proposes a multi-faceted framework that connects conceptual framing, disciplinary variations, technological evolution, and governance challenges. Specifically, it seeks to elucidate the meaning of Big Data in scientific research, consider its uneven impact due to disciplinary paradigms, analyze its developmental trajectory as a technological wave, and reflect on its role and influence in light of the practical challenges it poses.
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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 | 0.074 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.012 | 0.076 |
| Scholarly communication | 0.055 | 0.047 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".