Bibliographic record
Abstract
This chapter underscores the critical importance of addressing measurement errors in economic observations to uphold scientific integrity and foster progress. It emphasizes the need for economists to transparently report such errors from the outset of their careers and advocates for non-technical tools to enhance communication, including precise language and detailed data collection descriptions. The chapter promotes a culture of continuous improvement and error detection through data sensitivity analysis and triangulation. It discusses efforts to address inaccuracy in economic observations, highlighting the use of visual reporting and historical examples. The chapter further explores best practices in reporting data quality, using examples from Statistics Canada, the UK Office for National Statistics, CPB Netherlands’ World Trade Monitor and the Swedish Riksbank. It emphasizes the feasibility of transparent reporting on measurement error and calls for standardized practices. Lastly, the chapter stresses the importance of triangulation in research and considering multiple sources and methods for robust findings. It advocates for replication, structured reviews and meta-analyses as tools for evaluating literature and providing insights for researchers and policymakers.
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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 | 0.097 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".