ECIN Replication Package for "Replication of 'How Much Does Immigration Boost Innovation?'"
Bibliographic record
Abstract
Materials from my replication of Hunt & Gauthier-Loiselle (2010) (hereafter HGL). Folder "data" contains the dataset "finaldata.dta" obtained from original authors' replication files: https://www.openicpsr.org/openicpsr/project/114172/version/V1/view "2010_hgl_replication.Rproj" is the R project file for the analysis folder "code" contains the code used in the replication. "recreate_t7iv.R" contains code to rerun the instrumental variables estimates in R from HGL's table 7, and provide estimates when recreating the instrument from scratch. "recreate_table8.R" contains the code used to rerun the instrumental variables estimates in R from HGL's table 8, and provide estimates when recreating the instrument from scratch. "bw_analysis_t8.R" contains the code to check the robustness of HGL's results against the new diagnostic tests. "bw.cpp" contains the C++ code used to implement methods from Goldsmith-Pinkham et al. (2020) "tables.do" contains the Stata code used by HGL for their estimates in a single do file R packages used: tidyverse, haven, janitor, kableExtra, Rcpp, fixest, modelsummary, scales. Analysis conducted using R version 4.2.0
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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 | 0.019 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.490 | 0.197 |
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".