ESTIMATING TRENDS WITH A LINEAR MODEL: REPLY TO SAUER ET AL
Bibliographic record
Abstract
Sauer et al. (2004) advocate the use of trend estimation models that adjust counts for differences among observers. We agree that such adjustments are sometimes needed, and we noted (Bart et al. 2003) that they may readily be carried out prior to using the estimation method we described. Including observer covariates, however, is not always necessary and substantially reduces precision, as Sauer et al. (2004) acknowledge. Furthermore, under plausible conditions, including observer covariables introduces bias rather than removing it. In addition, the weighting scheme used in the estimating-equations approach may introduce bias. Our method avoids these sources of bias, is simpler and more flexible than the estimating- equations approach (e.g., carrying out power and sample-size calculations is much easier with our approach), and has smaller standard errors than the estimating-equations approach, especially when counts fluctuate widely. Model-based methods, including the estimating-equations approach, also have advantages, particularly in assessing complex influences on the counts. We recommend that analysts consider both approaches; comparing results obtained with the different methods may be especially informative. Estimación de Tendencias con un Modelo Lineal: Respuesta a Sauer et al Resumen. Sauer et al. (2004) recomiendan el uso de modelos de estimación de tendencias que ajusten los conteos a las diferencias existentes entre observadores. Nosotros estamos de acuerdo en que dichos modelos podrían ser útiles, y sugerimos que estos ajustes pueden incorporarse fácilmente antes de usar el mé todo de estimación que describimos. Nosotros introdujimos nuestro método porque es más sencillo y más flexible que el método que requiere estimar ecuaciones (e.g., realizar cálculos de poder estadístico y de tamaños de muestra es mucho más fácil con nuestro mé todo), y porque el nuestro se desempeñó mejor que el de estimación de ecuaciones cuando los conteos fluctuaron ampliamente. Adicionalmente, el procedimiento de pesaje usado en el método de estimación de ecuaciones podría introducir sesgos, mientras que el procedimiento lineal que nosotros describimos se pesa a sí mismo y no es susceptible a este error. Sin embargo, el método de estimación de ecuaciones también ofrece ventajas, particularmente en su habilidad para manejar modelos complejos. Recomendamos que los análisis consideren ambos procedimientos; comparar los resultados obtenidos mediante ambos métodos podría ser particularmente informativo.
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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 | 0.037 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.035 | 0.077 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".