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NU..."},"workflow":[{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:37:10Z","model":"sonnet","output":{"file":"data/data_dictionary.json","sha256":"5a9208e672af562222981cbec7678091f665206264bf982fdae7dd9cf299ff4a"},"outputs":[{"file":"data/data_dictionary.json","sha256":"5a9208e672af562222981cbec7678091f665206264bf982fdae7dd9cf299ff4a"},{"file":"design/spatial_design.json","sha256":"bb5dec86615e6dc3f2066e41a25f01351a0a0748363f00ae7a13cfacc65be28b"}],"phase":"initial","segment":0,"specialist":"data_architect","started":"2026-10-14T17:36:02Z","type":"specialist"},{"at":"2026-10-14T17:39:06Z","check":"contracts","detail":"data.db: data_dictionary.json declares tables that were not loaded — loaded without values: gdp_pps_nuts2.flag: 535 of 2826 non-null (needs 90%); nuts2_boundaries.mount_type: 263 of 299 non-null (needs 90%); nuts2_boundaries.urbn_type: 260 of 299 non-null (needs 90%); nuts2_boundaries.coast_type: 260 of 299 non-null (needs 90%); pop_nuts2.flag: 35 of 292 non-null (needs 90%). Tables in data.db: gdp_pps_nuts2 (2826 rows), gdp_pps_sample (236 rows), nuts2_boundaries (299 rows), pop_nuts2 (292 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","enforced":true,"passed":false,"phase":"initial","segment":0,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T17:39:06Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"c59811c7c668b3632ff52edf2e7949fcafb8b8727860879ab864f00eca249dad"},"outputs":[{"file":"data/data_summary.md","sha256":"c59811c7c668b3632ff52edf2e7949fcafb8b8727860879ab864f00eca249dad"},{"file":"results/summary_statistics.json","sha256":"45bcffb1da06e6ee4e8e30ccdbe10ddd60e93cfc746bf0ed1f70a2be8e954750"},{"file":"results/figure_spec.json","sha256":"e46671dadfbb99207219f68b7df0cdcaf670288f65ff3b61cd9dddfa8ef08057"}],"phase":"initial","segment":0,"specialist":"data_analyst","started":"2026-10-14T17:37:10Z","stopped_by":"contract violation: data.db: data_dictionary.json declares tables that were not loaded — loaded without values: gdp_pps_nuts2.flag: 535 of 2826 non-null (needs 90%); nuts2_boundaries.mount_type: 263 of 299 non-null (needs 90%); nuts2_boundaries.urbn_type: 260 of 299 non-null (needs 90%); nuts2_boundaries.coast_type: 260 of 299 non-null (needs 90%); pop_nuts2.flag: 35 of 292 non-null (needs 90%). Tables in data.db: gdp_pps_nuts2 (2826 rows), gdp_pps_sample (236 rows), nuts2_boundaries (299 rows), pop_nuts2 (292 rows). Load each series with `e2er-data ... --table <name>`; a table whose values are NULL was not loaded (check the connector's error, e.g. an API key).","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:39:33Z","model":"sonnet","output":{"file":"data/data_summary.md","sha256":"c59811c7c668b3632ff52edf2e7949fcafb8b8727860879ab864f00eca249dad","unchanged":true},"outputs":[{"file":"data/data_summary.md","sha256":"c59811c7c668b3632ff52edf2e7949fcafb8b8727860879ab864f00eca249dad","unchanged":true},{"file":"results/summary_statistics.json","sha256":"45bcffb1da06e6ee4e8e30ccdbe10ddd60e93cfc746bf0ed1f70a2be8e954750","unchanged":true},{"file":"results/figure_spec.json","sha256":"e46671dadfbb99207219f68b7df0cdcaf670288f65ff3b61cd9dddfa8ef08057","unchanged":true}],"phase":"initial","segment":0,"specialist":"data_analyst","started":"2026-10-14T17:39:06Z","type":"specialist"},{"at":"2026-10-14T17:39:33Z","check":"spatial_design","enforced":true,"passed":true,"phase":"initial","segment":0,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:40:53Z","model":"sonnet","output":{"file":"design/paper_plan.md","sha256":"3c700e61734f4d16dbce8e9bb329464e6a3388d33c8b6853be6609a9498ec4eb"},"phase":"initial","segment":0,"specialist":"idea_developer","started":"2026-10-14T17:39:33Z","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:41:24Z","model":"sonnet","output":{"file":"design/literature_review.md","sha256":"349c50d088e410d383ed806b996f83e706ce03a4c51af2e63f6e35327dbf33be"},"phase":"initial","segment":0,"specialist":"literature_scanner","started":"2026-10-14T17:39:33Z","type":"specialist"},{"at":"2026-10-14T17:43:34Z","check":"contracts","detail":"estimation_results.json: spatial results contract: spatial_statistics.moran_gdp_knn5_all244.expected -0.004115 is not -1/(n_units - 1) = -0.00425532; spatial_statistics.sens_drop_Ireland.expected -0.00431 is not -1/(n_units - 1) = -0.00425532; spatial_statistics.sens_drop_top5.expected -0.004348 is not -1/(n_units - 1) = -0.00425532; spatial_statistics.sens_drop_capital_regions.expected -0.004808 is not -1/(n_units - 1) = -0.00425532. This study reports spatial statistics and maps; write estimation_results.json as the data/spatial-results-schema skill specifies (from the script that computes it, never by hand).","enforced":true,"passed":false,"phase":"initial","segment":0,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T17:43:34Z","model":"sonnet","output":{"file":"design/econometric_spec.md","sha256":"c9ae7522be887de933d3c04211e502141679797b5eea812ff1fbadc98c94fb97"},"outputs":[{"file":"design/econometric_spec.md","sha256":"c9ae7522be887de933d3c04211e502141679797b5eea812ff1fbadc98c94fb97"},{"file":"results/estimation_results.json","sha256":"80bf50130e411841040da6ed9a15ca8e95b434bea95380f212f78b3d269b6ae6"}],"phase":"initial","segment":0,"specialist":"econometrics_specialist","started":"2026-10-14T17:41:24Z","stopped_by":"contract violation: estimation_results.json: spatial results contract: spatial_statistics.moran_gdp_knn5_all244.expected -0.004115 is not -1/(n_units - 1) = -0.00425532; spatial_statistics.sens_drop_Ireland.expected -0.00431 is not -1/(n_units - 1) = -0.00425532; spatial_statistics.sens_drop_top5.expected -0.004348 is not -1/(n_units - 1) = -0.00425532; spatial_statistics.sens_drop_capital_regions.expected -0.004808 is not -1/(n_units - 1) = -0.00425532. This study reports spatial statistics and maps; write estimation_results.json as the data/spatial-results-schema skill specifies (from the script that computes it, never by hand).","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:44:05Z","model":"sonnet","output":{"file":"design/econometric_spec.md","sha256":"c9ae7522be887de933d3c04211e502141679797b5eea812ff1fbadc98c94fb97","unchanged":true},"outputs":[{"file":"design/econometric_spec.md","sha256":"c9ae7522be887de933d3c04211e502141679797b5eea812ff1fbadc98c94fb97","unchanged":true},{"file":"results/estimation_results.json","sha256":"1f5f940107258897303648607ac6b6d9b7c5394c03430aefd37a2d4e31276d10"}],"phase":"initial","segment":0,"specialist":"econometrics_specialist","started":"2026-10-14T17:43:34Z","type":"specialist"},{"at":"2026-10-14T17:44:05Z","check":"spatial_results","detail":"(g) spatial_statistics.moran_gdp_knn5_all244: the reported Moran's I 0.27593 does not follow from spatial_units.csv and spatial_weights.csv (e2er computes 0.3705); (g) spatial_statistics.sens_drop_LU00: the reported Moran's I 0.41101 does not follow from spatial_units.csv and spatial_weights.csv (e2er computes 0.3705); (g) spatial_statistics.sens_drop_Ireland: the reported Moran's I 0.34177 does not follow from spatial_units.csv and spatial_weights.csv (e2er computes 0.3705); (g) spatial_statistics.sens_drop_top5: the reported Moran's I 0.42921 does not follow from spatial_units.csv and spatial_weights.csv (e2er computes 0.3705); (g) spatial_statistics.sens_drop_capital_regions: the reported Moran's I 0.6227 does not follow from spatial_units.csv and spatial_weights.csv (e2er computes 0.3705); (g) spatial_statistics.sens_within_country_demeaned: spatial_units.csv has no column gdp_pps minus national population-weighted mean; clusters.method needs a whole count","enforced":true,"passed":false,"phase":"initial","segment":0,"type":"check"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:44:56Z","model":"sonnet","output":{"file":"design/econometric_spec.md","sha256":"c9ae7522be887de933d3c04211e502141679797b5eea812ff1fbadc98c94fb97","unchanged":true},"outputs":[{"file":"design/econometric_spec.md","sha256":"c9ae7522be887de933d3c04211e502141679797b5eea812ff1fbadc98c94fb97","unchanged":true},{"file":"results/estimation_results.json","sha256":"ee650d15050ef8a1f63572bd018358b18b6ffa786e6d65a2c65a03f6c79ef396"}],"phase":"initial","segment":0,"specialist":"econometrics_specialist","started":"2026-10-14T17:44:05Z","type":"specialist"},{"at":"2026-10-14T17:44:56Z","check":"spatial_results","enforced":true,"passed":true,"phase":"initial","segment":0,"type":"check"},{"at":"2026-10-14T17:46:27Z","check":"contracts","detail":"paper_draft.tex: 2 construction(s) the researcher does not accept in a paper: \"Against this background\" in: Against this background, this paper asks how GDP per capita in PPS varies across EU NUTS-2 regions in 2024 and how strongly regions with high and low values cluster in space. (pivot to the contribution directly (for instance: To keep these quantities apart, we propose ...)); \"rather than\" in: Capital regions are typically income peaks surrounded by poorer neighbours, so they lower rather than create autocorrelation, and the clustering that remains is that of the broad, contiguous income belts. (state the positive claim and stop). Rewrite each of these sentences without the construction; keep its content","enforced":true,"passed":false,"phase":"initial","segment":0,"type":"check"},{"accepted":false,"backend":"claude_code","ended":"2026-10-14T17:46:27Z","model":"sonnet","output":{"file":"paper/paper.tex","sha256":"1910d43eba8b417d3a5de6955813fd7865efdd338331799ede101b00706c6c44"},"outputs":[{"file":"paper/paper.tex","sha256":"1910d43eba8b417d3a5de6955813fd7865efdd338331799ede101b00706c6c44"},{"file":"results/table_spec.json","sha256":"127b8bd0a97eebf08220edb8db4516d0d0b3320db2d3086ca0479d6367d17fd5"}],"phase":"initial","segment":0,"specialist":"paper_drafter","started":"2026-10-14T17:44:56Z","stopped_by":"contract violation: paper_draft.tex: 2 construction(s) the researcher does not accept in a paper: \"Against this background\" in: Against this background, this paper asks how GDP per capita in PPS varies across EU NUTS-2 regions in 2024 and how strongly regions with high and low values cluster in space. (pivot to the contribution directly (for instance: To keep these quantities apart, we propose ...)); \"rather than\" in: Capital regions are typically income peaks surrounded by poorer neighbours, so they lower rather than create autocorrelation, and the clustering that remains is that of the broad, contiguous income belts. (state the positive claim and stop). Rewrite each of these sentences without the construction; keep its content","type":"specialist"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:46:46Z","model":"sonnet","output":{"file":"paper/paper.tex","sha256":"e6ddc6dc10f4628ac28b105f86a5abc108f3de1ce7d1eccf5fbf90a774b78291"},"outputs":[{"file":"paper/paper.tex","sha256":"e6ddc6dc10f4628ac28b105f86a5abc108f3de1ce7d1eccf5fbf90a774b78291"},{"file":"results/table_spec.json","sha256":"127b8bd0a97eebf08220edb8db4516d0d0b3320db2d3086ca0479d6367d17fd5","unchanged":true}],"phase":"initial","segment":0,"specialist":"paper_drafter","started":"2026-10-14T17:46:27Z","type":"specialist"},{"action":"send_back","at":"2026-10-14T17:47:50Z","phase":"initial","remark":"Corrections to paper_draft.tex, no new analysis: (1) Title: 'Rich Cores and Poor Peripheries' contradicts the paper's own finding (the clustering is national blocks; within countries none). Use a plain descriptive title, e.g. 'Spatial clustering of GDP per inhabitant across EU NUTS-2 regions, 2024'. (2) Abstract: there are three alternative weights (5 and 8 nearest neighbours, inverse distance), not four; the abstract's local clusters are uncorrected - add that after the false discovery rate correction 22 remain (20 low-low, one high-high, one high-low). (3) Introduction: 'the richest tenth of regions earns roughly 2.4 times the income of the poorest tenth' is wrong; say the 90th percentile is 2.4 times the 10th. (4) Local clusters: delete 'because the richest regions are fewer and more heterogeneous' (not tested). (5) Capital regions: the 27 dropped include whole single-region countries (EE00, LV00, LU00, CY00, MT00), and the drop leaves 3 regions without a neighbour (1 in the top-five variant); say both, and present 'income peaks surrounded by poorer neighbours' as a reading, not a finding. (6) Discussion: the demeaned Moran's I (0.020, p = 0.6) shows that regions' deviations from their national mean are not clustered; it does not test cross-border gradients - rewrite the sentence about 'cross-border gradients that a smooth core-periphery narrative would imply'. (7) Data: name the datasets (nama_10r_2gdp, unit PPS_EU27_2020_HAB; demo_r_d2jan) and add the sentence Eurostat's terms ask for: the statistics are computed by the authors from Eurostat data; Eurostat is not responsible for them. Keep the EuroGeographics notice in every map caption.","segment":0,"step":"initial","target":"paper_drafter","type":"researcher"},{"accepted":true,"backend":"claude_code","ended":"2026-10-14T17:48:26Z","model":"sonnet","output":{"file":"paper/paper.tex","sha256":"d954a0cf0922b724180dd5e0229b42d41e285f9b332ce8091205b828e7373c93"},"outputs":[{"file":"paper/paper.tex","sha256":"d954a0cf0922b724180dd5e0229b42d41e285f9b332ce8091205b828e7373c93"},{"file":"results/table_spec.json","sha256":"127b8bd0a97eebf08220edb8db4516d0d0b3320db2d3086ca0479d6367d17fd5","unchanged":true}],"phase":"initial","segment":1,"sent_back":{"at":"2026-10-14T17:47:50Z","remark":"Corrections to paper_draft.tex, no new analysis: (1) Title: 'Rich Cores and Poor Peripheries' contradicts the paper's own finding (the clustering is national blocks; within countries none). Use a plain descriptive title, e.g. 'Spatial clustering of GDP per inhabitant across EU NUTS-2 regions, 2024'. (2) Abstract: there are three alternative weights (5 and 8 nearest neighbours, inverse distance), not four; the abstract's local clusters are uncorrected - add that after the false discovery rate correction 22 remain (20 low-low, one high-high, one high-low). (3) Introduction: 'the richest tenth of regions earns roughly 2.4 times the income of the poorest tenth' is wrong; say the 90th percentile is 2.4 times the 10th. (4) Local clusters: delete 'because the richest regions are fewer and more heterogeneous' (not tested). (5) Capital regions: the 27 dropped include whole single-region countries (EE00, LV00, LU00, CY00, MT00), and the drop leaves 3 regions without a neighbour (1 in the top-five variant); say both, and present 'income peaks surrounded by poorer neighbours' as a reading, not a finding. (6) Discussion: the demeaned Moran's I (0.020, p = 0.6) shows that regions' deviations from their national mean are not clustered; it does not test cross-border gradients - rewrite the sentence about 'cross-border gradients that a smooth core-periphery narrative would imply'. (7) Data: name the datasets (nama_10r_2gdp, unit PPS_EU27_2020_HAB; demo_r_d2jan) and add the sentence Eurostat's terms ask for: the statistics are computed by the authors from Eurostat data; Eurostat is not responsible for them. 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