{"id":"W4391052391","doi":"10.1177/00018392231221070","title":"License to Broker: How Mobility Eliminates Gender Gaps in Network Advantage","year":2024,"lang":"en","type":"article","venue":"Administrative Science Quarterly","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tsinghua University; McGill University; Universiteit van Tilburg; Emory University; George Mason University","keywords":"License; Business; Institution; Gender gap; Interpersonal ties; Strong ties; Affect (linguistics); Social mobility; Demographic economics; Public relations; Industrial organization; Economics; Computer science; Psychology; Political science; Social psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003762429,0.0001868356,0.0002089535,0.0001936327,0.0008359982,0.0008064401,0.0007145078,0.00009265963,0.000224863],"category_scores_gemma":[0.0002400963,0.0001823204,0.00008683493,0.00234165,0.001341121,0.001572125,0.00004095538,0.000265669,0.0002253133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003026447,"about_ca_system_score_gemma":0.00119658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005249066,"about_ca_topic_score_gemma":0.003978584,"domain_scores_codex":[0.9968002,0.0003583301,0.0002390131,0.0008370219,0.000846424,0.0009189505],"domain_scores_gemma":[0.9987273,0.0002883551,0.00004920954,0.0003123144,0.0001756923,0.0004471962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00009396428,0.0003894735,0.02087881,0.0001029321,0.00002625415,0.0002413728,0.6401267,0.00003135946,0.002790221,0.3084959,0.004920778,0.02190218],"study_design_scores_gemma":[0.0003368107,0.002093963,0.1362761,0.0001518684,0.00003018016,0.000007614633,0.7812306,0.0003680952,0.001128689,0.02683157,0.05055469,0.0009898641],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9346975,0.0002378164,0.0008547185,0.008249774,0.001205486,0.0006979715,0.00004653963,0.0001922213,0.05381798],"genre_scores_gemma":[0.9975471,0.00001182236,0.0007743086,0.0003763811,0.0001934738,0.00002165662,0.000002706769,0.000006237533,0.001066347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2816644,"threshold_uncertainty_score":0.777652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09311765695543603,"score_gpt":0.3742105843328785,"score_spread":0.2810929273774425,"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."}}