A Scarcity Literature Mischaracterized with an Empirical Audit
Bibliographic record
Abstract
O’Donnell et al. (“ODAL”) (1) claim to audit the “scarcity literature” through a series of replications. Although we applaud the audit’s goals, we found serious issues that invalidate its conclusions. Notably, the paper fails as an audit of the scarcity literature. (1) It includes studies that are not about resource scarcity and even studies that are not about scarcity at all. (2) It fails to implement its own stated selection criteria. (3) It contains analysis errors. (4) Methodological issues make several of the replications untrue to the original. These issues affect at least 11 out of their 20 studies (see Figure 1, for a detailed discussion, see 2), calling into question the overall conclusions.
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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 | 0.072 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".