What impact does the choice of formula have on international comparisons?
Bibliographic record
Abstract
In this paper an empirical comparison of a number of alternative multilateral index‐number formulae is undertaken. The magnitude of the effect of choosing one formula over another is ascertained using an appropriate cross‐sectional data set constructed under the auspices of the Eurostat‐OECD Purchasing Power Parity Programme. To this end, a new indicator is proposed that facilitates the measurement of the difference between two sets of bloc consumption shares, each computed using a different multilateral comparison method. JEL Classification: C31, C43, C81, E31, F31, O57 Quel impact est‐ce que le choix des formules a sur les comparaisons internationales? Ce mémoire propose une comparaison empirique d'un certain nombre de formules de nombres‐indices multilatéraux. On tente de jauger la magnitude de l'impact du choix d'une formule plutôt qu'une autre en utilisant une base de données transversales appropriée construite sous l'égide du Programme de parité du pouvoir d'achat Eurostat‐OCDE. On propose un nouvel indicateur qui facilite la mesure de la différence entre deux ensembles de patterns de consommation, chacun calculéà l'aide d'une méthode de comparaison multilatérale différente.
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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 | 0.049 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".