Bibliographic record
Abstract
The Framework Economic theory rarely dictates a specific functional form. Instead, it typically specifies a collection of potentially related variables and general functional properties of the relationship. For example, economic theory may imply that the impact of a given variable is positive or negative (monotonicity), that doubling of prices and incomes should not alter consumption patterns (homogeneity of degree zero), that a proportionate increase in all inputs will increase output by the same proportion (constant returns to scale or, equivalently, homogeneity of degree one), that the effect of one variable does not depend on the level of another (additive separability), that the relationship possesses certain curvature properties such as concavity or convexity, or that observed consumption patterns result from optimization of utility subject to a budget constraint (the maximization hypothesis). Empirical investigation is then required to assess whether one or another variable is significant or whether a particular property holds. In parametric regression modeling, a functional form is selected and properties are tested by imposing restrictions on the parameters. However, rejection of a hypothesis may be a consequence of the specific functional form that has been selected (but not implied by economic theory). Thus, although the translog production function is richer and more flexible than the Cobb–Douglas, it may not capture all the interesting features of the production process and may indeed lead to incorrect rejection of restrictions. Nonparametric procedures, on the other hand, provide both richer families of functions and more robust tests for assessing the implications of economic theory. Within this framework it is also possible to test whether a specific parametric form is adequate.
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 | 0.033 | 0.191 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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".