Upload v4-HOT/socratic/pilot/20260524_110540.json with huggingface_hub

v4-HOT/socratic/pilot/20260524_110540.json

@@ -4,7 +4,7 @@
   "condition": "socratic",
   "run_mode": "pilot",
   "started_at": "2026-05-24T11:05:40.640237+10:00",
-  "ended_at": "2026-05-24T11:08:01.264786+10:00",
+  "ended_at": "2026-05-24T11:08:07.288462+10:00",
   "model": "gpt-5.4-nano-2026-03-17",
   "case_id": "ai-predictive-analytics",
   "perfect_answer": {
@@ -64,6 +64,14 @@
       "directive": "Stay on your “real opt-out” standard. Anchor on their idea of “verifiable process” that removes student data and uses third-party audits. Push to sharpen what “not included in predictive analyses” concretely means at the system level: what specific logs/feeds/feature pipelines must be blocked so the refusal actually changes model inputs and teacher-facing outcomes. End on one open question about what they’d require as the minimum proof of exclusion.",
       "reason": "They added concrete mechanisms (audits/third-party oversight) for making refusal effective and verifiable, continuing the opt-out thread.",
       "latency_ms": 2539
+    },
+    {
+      "live_subprobe_id": 2,
+      "advanced_this_turn": true,
+      "turn_type": "transition-due",
+      "directive": "ENGAGED_MODE. In Block 1, credit the work they did: they made “real choice” concrete by requiring pipeline blocks that stop opted-out data reaching both model inputs and teacher-facing scores, plus an audit-and-proof standard. No need to judge whether it’s right. In Block 2, shift angles to what teachers actually see: name the difference between “students receive tips” and “teachers see a prediction,” then ask which part should change what teachers do toward students and why.",
+      "reason": "sub-trigger 2b: prior engaging-default count on sub-probe 1 = 3 (turns: 1, 3, 6); ENGAGED_MODE",
+      "latency_ms": 3281
     }
   ],
   "turns": [
@@ -222,6 +230,32 @@
         "main_ms": 8906,
         "total_ms": 11450
       }
+    },
+    {
+      "n": 7,
+      "timestamp": "2026-05-24T11:08:07.288452+10:00",
+      "user_message": "Yes, I think we would indeed need specific pipeline blocks to ensure that students who opt out are completely separated from predictive models and teacher-facing scores. For independent audits, I’d want proof that those blocks were in place—perhaps a detailed report showing that no data from opted-out students was processed, along with spot checks of the system to verify compliance. Transparency in that reporting would be essential to maintain trust.",
+      "manager_verdict": {
+        "live_subprobe_id": 2,
+        "advanced_this_turn": true,
+        "turn_type": "transition-due",
+        "directive": "ENGAGED_MODE. In Block 1, credit the work they did: they made “real choice” concrete by requiring pipeline blocks that stop opted-out data reaching both model inputs and teacher-facing scores, plus an audit-and-proof standard. No need to judge whether it’s right. In Block 2, shift angles to what teachers actually see: name the difference between “students receive tips” and “teachers see a prediction,” then ask which part should change what teachers do toward students and why.",
+        "reason": "sub-trigger 2b: prior engaging-default count on sub-probe 1 = 3 (turns: 1, 3, 6); ENGAGED_MODE",
+        "latency_ms": 3281
+      },
+      "plan_control": "=== PLAN CONTROL (host-managed, authoritative) ===\nturn_type: transition-due\nlive_subprobe_id: 2\nadvanced_this_turn: True\n\nLIVE SUB-PROBE (work this one this turn):\nid: 2\nfamily: Duty and rights (focus on consent and who gets shaped by the prediction)\nprobe: Tell apart “students receive tips” from “teachers see a prediction,” and ask which part changes how people treat each other\nrationale: The harms the draft worries about mostly come from teacher perception. Students can still be helped, but only the part that reaches teachers needs extra scrutiny and tighter limits.\nanchor: \"rules about who sees the scores\"\n\nDIRECTIVE for this turn:\nENGAGED_MODE. In Block 1, credit the work they did: they made “real choice” concrete by requiring pipeline blocks that stop opted-out data reaching both model inputs and teacher-facing scores, plus an audit-and-proof standard. No need to judge whether it’s right. In Block 2, shift angles to what teachers actually see: name the difference between “students receive tips” and “teachers see a prediction,” then ask which part should change what teachers do toward students and why.\n=== END PLAN CONTROL ===",
+      "assistant_response": "That was a good thread to work through. You made “real choice” concrete by requiring pipeline blocks that prevent opted out students’ activity from reaching both the model and any teacher facing scores, and you added an audit and proof standard to verify it is actually happening.\n\nNow turning to a different piece, we can separate “students receive tips” from “teachers see a prediction.” Which part, in your view, should most strongly shape how teachers treat students, and why?",
+      "scratch": {
+        "chosen_target": 2,
+        "response_summary": "Credited their concrete opt-out pipeline and audit proof approach, then shifted to the teacher-facing distinction and asked which part should shape teacher treatment.",
+        "directive_followed": "yes"
+      },
+      "latency_s": {
+        "manager_ms": 3282,
+        "ttft_ms": 1280,
+        "main_ms": 2731,
+        "total_ms": 6018
+      }
     }
   ],
   "feedback": [],
@@ -317,17 +351,21 @@
   },
   "upload_errors": [],
   "session_summary": {
-    "n_turns": 6,
-    "n_manager_verdicts": 6,
-    "n_transitions": 0,
+    "n_turns": 7,
+    "n_manager_verdicts": 7,
+    "n_transitions": 1,
     "n_plan_completes": 0,
     "subprobe_coverage": {
       "1": {
         "engaging_default": 3,
         "other": 3
+      },
+      "2": {
+        "engaging_default": 0,
+        "other": 1
       }
     },
-    "last_subprobe_visited": 1,
+    "last_subprobe_visited": 2,
     "total_subprobes_in_plan": 9,
     "outcome": "ran-without-plan-complete"
   }