Data mining and the econometrics industry: comments on the papers of Mayer and of Hoover and Perez
Bibliographic record
Abstract
We maintain that the actions of researchers show that data mining is a necessary part of econometric inquiry. We analyse this phenomenon using the analogy of an industry producing a product (econometric analyses). There is a risk of selective reporting as Mayer indicates but we argue that other researchers (competition) will ensure that the sensitivity of truly important findings is checked. Hence, initial researchers have an incentive to analyse sensitivity from the beginning and so produce a quality product. Some suggestions are made towards encouraging this process. The 'general to specific' approach to data mining as promoted by Hoover and Perez can be valuable but it is premature to eliminate other strategies.
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 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 | 0.044 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.036 | 0.047 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".