Bibliographic record
Abstract
Having a formal understanding of what various institutions represent should be important. There are few economic variables which can accumulate over time (and give us what i call as "state utility"). All these institutions, like education, health, social (like caste system, dowry etc.), do is change the way these state variables evolve. Government affects these institutions by investing in them which changes the rate of evolvement (via advances in technology or infrastructure or formal laws etc) There are no "good" or "bad" institutions. There are institutions which are "in-line" with the people's preferences and there are the ones who are "mismatch" with what people want. As economist, we may think that since caste system etc. are not beneficial in "monetary" terms, they are bad for growth. But comments like "i PREFER being hungry than borrow money from some lower caste person (to start new business)" should make us realize that these institutions are not like some mysterious forces. They represent the aggregate level mechanism by which people let their preferences known and how these preferences evolve. We should not force the kind of development (i.e. kind of institutions), we (policy makers) want them to have. May be that is not what people want. Hence, having a formal understanding of these institutions and the mechanisms through which government can know about these "preferred" institutions becomes important.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 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".