Bibliographic record
Abstract
Abstract The purpose of this essay is to discuss two approaches to inference and how “human capital” can provide a way to combine them. The first approach, ubiquitous in economics, is based upon the Rubin–Holland potential outcomes model and relies upon randomized treatment to measure the causal effect of choice. The second approach, widely used in the pattern recognition and machine learning literatures, assumes that choice conditional upon current information is optimal (or at least high quality), and then provides techniques to generalize observed choice to new cases. The “human capital” approach combines these methods by using observed decisions by experts to reduce the dimensionality of the feature space and allow the categorization of decisions by their propensity score. The fact that the human capital of experts is heterogeneous implies that errors in decision making are inevitable. Moreover, under the appropriate conditions, these decisions are random conditional upon the propensity score. This in turn allows us to identify the conditional average treatment effect for a wider class of situations than would be possible with randomized control trials. This point is illustrated with data from medical decision making in the context of treating depression, heart disease and adverse childbirth events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".