Bibliographic record
Abstract
This short article is part of an online symposium in which law and development scholars were invited to respond to a set of questions posed by the conference organizer. In response to the question “what is the future of law and development?”, this article discusses different approaches in law and development studies. First, it suggests that the “context matters” approach is helpful in understanding the limitations of the “toolkit” approach, but while it helps us understand what went wrong in failed attempt to promote institutional reforms, it fails to provide guidance for future improvements. Second, the article discusses a “what works” approach, which has been suggested by some of the participants of the symposium as a fruitful way to overcome the limits of the existing approaches. While this approach seem indeed very promising, this article argues that there are some issues that need to be considered before we adopt it as a panacea for the shortcomings of law and development studies.
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 | 0.122 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.026 | 0.203 |
| Scholarly communication | 0.050 | 0.081 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.029 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".