Bibliographic record
Abstract
A legal researcher must see that research is an activity. The research is not only reading books, principles, doctrines, and regulations but also an activity to find data. Legal research should no longer distinguish between normative research and sociological research, or qualitative and quantitative research. This research method uses focus group discussions as used in qualitative research. The results of the study are that the law was born from the community that the legal system consists of substance, system, and culture. So that legal research that has its characteristics and is different from social science (sui generis) needs to be re-examined in its meaning in research. Related to the use of primary data existence, in socio-legal research requires primary data whose ranking consists of 7 (seven), namely: Dissertation, National and International scientific journal articles, Thesis and Thesis, Interview, Academic Paper, Court Verdict and Case, which how to obtain primary data must be systematic, scientific and rational. So in addition to normative juridical research with the object of research on legal principles, teachings or legal theories, and legal doctrines, legal research needs to reposition primary data in socio-legal 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 | 0.127 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".