Artificial intelligence and big data-driven evaluation research and practices: A systematic literature review
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
The widespread adoption of digitalization and artificial intelligence, alongside the abundance of big data, has significantly transformed societies. Recently, there has been an increasing interest in leveraging big data and artificial intelligence to capture and analyze social transformative change in evaluation. However, there is no consensus on the ethical and appropriate use of these tools in evaluation. This article used a systematic literature review to provide an overview of using big data and artificial intelligence for evaluation purposes, identifying challenges faced. Unresolved issues encompass ethical, methodological, and ownership concerns. The study suggests ways to address these challenges and advocates for united efforts to mix big data and artificial intelligence with traditional approaches. To achieve this, it emphasizes the necessity of leveraging interconnected data platforms, mitigating ethical risks, and enhancing evaluators’ competencies in computer and data science, which is essential for the integration of big data and artificial intelligence in the evaluation field.
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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it