Bibliographic record
Abstract
The article presents a commentary on the accompanying six papers from the perspective of a cognitive psychologist. Treisman's (1964, 1969) levels of analysis model of selective attention is suggested as a framework within which the interactions between 'bottom-up' auditory factors and 'top-down' cognitive factors may be understood. The complementary roles of auditory and cognitive aspects of hearing are explored, and their mutually compensatory properties discussed. The findings and ideas reported in the six accompanying papers fit well into such a 'levels of processing' framework, which may therefore be proposed as a model for understanding the effects of aging on speech processing and comprehension. El artículo presenta un comentario sobre los seis trabajos acompañantes desde la perspectiva de un psicólogo de la cognición. Se sugiere el modelo de Treisman (1964, 1969) de niveles de análisis de la atención selectiva como el marco dentro del cuál las interacciones entre los factores de atención "de abajo hacia arriba" y los factores cognitivos de "de arriba hacia abajo" pueden ser comprendidos. Se exploran los papeles complementarios de los aspectos auditivos y cognitivos de la audición y se discuten sus mutuas propiedades compensatorias. Los hallazgos e ideas reportados en los seis trabajos acompañantes calzan bien en dicho marco de "niveles de procesamiento", los que puede, por tanto, ser propuestos como un modelo para comprender el efecto del envejecimiento para el procesamiento y la comprensión del lenguaje.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".