Should we be wary of using artificial intelligence-based big data management in social research?
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
This study examines the future role of artificial intelligence (AI) in transforming research processes within the social sciences, focusing on how AI may redefine researchers' responsibilities and potentially replace human participants in certain types of studies. Employing the Delphi method, the study collects expert opinions to evaluate both facilitating factors and barriers to the integration of AI into scientific research. Key findings indicate that while technological advancements – such as open-access data and the integration of AI with existing research tools – support the growing role of AI, significant challenges remain. These include the difficulty of verifying AI-generated information and concerns regarding authenticity in AI-driven research. Social factors, particularly the risk of excessive reliance on AI leading to diminished originality, emerged as critical barriers. In contrast, economic considerations, such as declining development costs, were viewed as less influential. The study’s practical implications include the need for robust ethical guidelines and enhanced AI training for researchers. By offering original insights into the evolving intersection of AI and social science research, this study highlights both the transformative potential of AI and the urgent need for its responsible integration to preserve research integrity and reliability.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it