{"id":"W7125986751","doi":"10.3886/e224781v1-198178","title":"Data and Code for: Across-Country Wage Compression in Multinationals","year":2025,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Compression (physics); Code (set theory); Wage; Data compression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001509956,0.001686062,0.001092468,0.004443289,0.000821768,0.002423582,0.00247944,0.001799329,0.1821544],"category_scores_gemma":[0.009682471,0.0009364639,0.001245616,0.01000891,0.0004309554,0.001512174,0.001911164,0.00206462,0.1500581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166319,"about_ca_system_score_gemma":0.00269808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06397176,"about_ca_topic_score_gemma":0.07597595,"domain_scores_codex":[0.9984655,0.0002226778,0.0002044693,0.0003289421,0.0004065962,0.0003718434],"domain_scores_gemma":[0.9955617,0.0008806667,0.0007959066,0.001011495,0.001319988,0.0004302487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003069509,0.00001678678,0.0009590021,0.0001481806,0.00001216765,0.000007289145,0.00002518894,0.0001780155,0.00001946042,0.0003636705,0.9967496,0.001489797],"study_design_scores_gemma":[0.0003405521,0.00001986024,0.01668059,0.0003621495,0.00002178861,0.00004387711,0.0003422921,0.0004589548,0.0002453045,0.001583813,0.9798459,0.0000549248],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000140563,0.00001755346,0.0000426156,0.0000561769,0.00001601886,0.000009906142,0.9989673,0.0001349314,0.000615013],"genre_scores_gemma":[0.0006538437,0.00003311609,0.000302719,0.00006240439,0.000008483707,0.0001272048,0.9969485,0.0001178658,0.001745788],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1821544,"threshold_uncertainty_score":0.609367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1059329578366411,"score_gpt":0.4352647582031024,"score_spread":0.3293318003664613,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}