ECIN Replication Package for "Replication of 'How Much Does Immigration Boost Innovation?'"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div>Materials from my replication of Hunt & Gauthier-Loiselle (2010) (hereafter HGL). </div><div> </div><div><span>Folder "data" contains the dataset "finaldata.dta" obtained from original authors' replication files: <a target="_blank" rel="nofollow" href="https://www.openicpsr.org/openicpsr/project/114172/version/V1/view">https://www.openicpsr.org/openicpsr/project/114172/version/V1/view</a></span> </div><div><br></div><div>"2010_hgl_replication.Rproj" is the R project file for the analysis folder "code" contains the code used in the replication. </div><ul><li>"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.</li><li>"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.</li><li>"bw_analysis_t8.R" contains the code to check the robustness of HGL's results against the new diagnostic tests.</li><li>"bw.cpp" contains the C++ code used to implement methods from Goldsmith-Pinkham et al. (2020)</li><li>"tables.do" contains the Stata code used by HGL for their estimates in a single do file</li></ul><div> R packages used: tidyverse, haven, janitor, kableExtra, Rcpp, fixest, modelsummary, scales.</div><div><br></div><div>Analysis conducted using R version 4.2.0<br></div><br>
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it