Bibliographic record
Abstract
This paper presents a flexible functional form called third-order translog, which includes higher-order terms, to estimate systems of budget-share equations using Canadian crosssectional micro-data. We test the statistical significance of the third-order terms, and also test regularity conditions such as homogeneity and symmetry restrictions of the budgetshare systems. It is important to test these restrictions, since their rejection might imply that our data does not support the theory of utility maximization or the particular functional form used in the model is flawed. We find that the third-order terms are statistically significant which means that they are important determinant of consumer demand. But we reject the regularity conditions for most of the demographic groups. We also find that our model suffers from heteroscedastic errors and repeat the tests using “Heteroscedastic Consistent Covariance Matrix Estimator (HCCME).” The third-order terms are once again found to be significant but the regularity conditions fail to hold for all the demographic groups. The rejection of regularity conditions indicate a need for proper aggregation restrictions and determining the “neighborhood” of the observation space where the regularity conditions can hold.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".