Autism Research Funding Allocation: Can Economics Tell Us If We Have Got It Right?
Bibliographic record
Abstract
There is a concern that the allocation of autism spectrum disorder (ASD) research funding may be misallocating resources, overemphasizing basic science at the expense of translational and clinical research. Anthony Bailey has proposed that an economic evaluation of autism research funding allocations could be beneficial for funding agencies by identifying under- or overfunded areas of research. In response to Bailey, we illustrate why economics cannot provide an objective, technical solution for identifying the "best" allocation of research resources. Economic evaluation has its greatest power as a late-stage research tool for interventions with identified objectives, outcomes, and data. This is not the case for evaluating whether research areas are over- or underfunded. Without an understanding of how research funding influences the likelihood and value of a discovery, or without a statement of the societal objectives for ASD research and level of risk aversion, economic analysis cannot provide a useful normative evaluation of ASD research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.147 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.040 |
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; both teacher heads agree on what is shown here.
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".