The benchmarking strategy has a role to play across cultures.
Bibliographic record
Abstract
This article responds to the commentary by Cardemil (2015) on our original article (Spilka & Dobson, 2015), in which we proposed the use of a benchmarking strategy to evaluate culturally adapted and transported treatments. We address Cardemil's assertion that a culturally embedded or bottom-up approach to the development of models of psychopathology and treatment of disorders is optimal and argue that benchmarking provides an alternative model in which treatments may also be developed in one culture and exported, with appropriate adaptation and evaluation, to another. We discuss the circumstances in which benchmarking is likely to have enhanced benefit and argue that this issue should be addressed with research and evidence as part of the global efforts toward evidence-based practice.
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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 | 0.253 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.058 |
| Scholarly communication | 0.024 | 0.042 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.029 | 0.047 |
| Insufficient payload (model declined to judge) | 0.005 | 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".