Science and the Scientific Nature of Research in the Social Sciences
Bibliographic record
Abstract
There have been a lot of arguments regarding the scientific nature of the social science disciplines. While some argue that the social sciences are not scientific in nature due to their areas of operations and the nature of their subject matter, others, particularly the social scientists themselves, argue that despite the claims of the ‘pure’ sciences, they still abstract from the characteristics of science, involve themselves in rigorous scientific experimentation and investigations and adhere to the principles of scientific laid down rules of research processes. This paper on the scientific nature of the social sciences explores these schools of thought and evaluates the processes of social research and investigation to survey how scientific they are and how they apply as scientific. This paper also explores the aims of science, the characteristics of science, the principles of social sciences, the common tools of research of the social sciences, the social science research design and the stages of social research. The essence of the reviews is to examine their scientific nature and to argue if they conform to the logic of scientific research.
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 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 | 0.133 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.008 | 0.112 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".