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v4-HOT/explanatory/pilot/20260524_111123.json

@@ -4,7 +4,7 @@
   "condition": "explanatory",
   "run_mode": "pilot",
   "started_at": "2026-05-24T11:11:23.803579+10:00",
-  "ended_at": "2026-05-24T11:12:08.696297+10:00",
+  "ended_at": "2026-05-24T11:12:16.919704+10:00",
   "model": "gpt-5.4-nano-2026-03-17",
   "case_id": "ai-predictive-analytics",
   "perfect_answer": {
@@ -32,6 +32,14 @@
       "directive": "Stay on the thread of “that label sticks, teachers see it, treat the student differently.” Anchor on their line about students internalizing the label. Push one step toward the long-run mechanism: not just first-week behavior, but what changes over a semester or school year (expectations, help offered, opportunities tried). Land on one open question about what concrete later outcome proves the label is self-fulfilling, not just a hypothesis.",
       "reason": "The participant argues the premise is wrong and reiterates the self-fulfilling prophecy risk (“labeled as ‘at-risk’ may internalize that label and underperform”).",
       "latency_ms": 2454
+    },
+    {
+      "live_subprobe_id": 2,
+      "advanced_this_turn": true,
+      "turn_type": "transition-due",
+      "directive": "ENGAGED_MODE. Recap their tightened thread: the label doesn’t just change first-week teacher behavior—it can lock in lower expectations and channel students into fewer opportunities, reinforcing existing fairness gaps over time. Credit how they moved from “teacher interactions” to “systemic reinforcement” that alters a student’s trajectory. In Block 2, name the next angle in plain words: who pays the costs for wrong calls versus who benefits from the school reporting the system helps—then stop (teaching happens next).",
+      "reason": "sub-trigger 2b: prior engaging-default count on sub-probe 1 = 2 (turns: 1, 2); current classified as engaging-default; ENGAGED_MODE",
+      "latency_ms": 3874
     }
   ],
   "turns": [
@@ -86,6 +94,32 @@
         "main_ms": 4327,
         "total_ms": 6789
       }
+    },
+    {
+      "n": 3,
+      "timestamp": "2026-05-24T11:12:16.919690+10:00",
+      "user_message": "You're missing the broader implications of this label's impact, which isn't just about teacher interactions but about the systemic reinforcement of biases within the educational framework. The AI system's predictions can perpetuate disparities, leading to some students being funneled into lower expectations and opportunities, regardless of their actual potential. This is not merely an adjustment period; it's a dangerous cycle that can fundamentally alter a student's educational trajectory.",
+      "manager_verdict": {
+        "live_subprobe_id": 2,
+        "advanced_this_turn": true,
+        "turn_type": "transition-due",
+        "directive": "ENGAGED_MODE. Recap their tightened thread: the label doesn’t just change first-week teacher behavior—it can lock in lower expectations and channel students into fewer opportunities, reinforcing existing fairness gaps over time. Credit how they moved from “teacher interactions” to “systemic reinforcement” that alters a student’s trajectory. In Block 2, name the next angle in plain words: who pays the costs for wrong calls versus who benefits from the school reporting the system helps—then stop (teaching happens next).",
+        "reason": "sub-trigger 2b: prior engaging-default count on sub-probe 1 = 2 (turns: 1, 2); current classified as engaging-default; ENGAGED_MODE",
+        "latency_ms": 3874
+      },
+      "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: Consequences and how harm spreads (label effects and long-term fallout)\nprobe: Think about who pays the cost when the system is wrong, compared with who gets the benefit the school reports\nrationale: The argument says the system is harmful, but it needs sharper who-costs-versus-who-benefits reasoning. This helps make the harm feel real and non-optional for affected students.\nanchor: \"Once you label a student… that label sticks\"\n\nDIRECTIVE for this turn:\nENGAGED_MODE. Recap their tightened thread: the label doesn’t just change first-week teacher behavior—it can lock in lower expectations and channel students into fewer opportunities, reinforcing existing fairness gaps over time. Credit how they moved from “teacher interactions” to “systemic reinforcement” that alters a student’s trajectory. In Block 2, name the next angle in plain words: who pays the costs for wrong calls versus who benefits from the school reporting the system helps—then stop (teaching happens next).\n=== END PLAN CONTROL ===",
+      "assistant_response": "Pulling those threads together on how the label spreads: the strongest version of your point is that it does not just nudge first-week teacher interactions, it can lock in lower expectations and fewer opportunities, which then reinforces existing fairness gaps over time. Your move from “teacher behavior changes” to “systemic reinforcement that alters a student’s trajectory” is what makes the harm feel more structural than temporary.  \n\nNow turning to a different piece: who pays the costs for wrong calls, compared with who gets the benefits the school reports.",
+      "scratch": {
+        "chosen_target": 2,
+        "response_summary": "Recapped the label-as-long-term-reinforcement thread and named the next angle about costs for wrong calls versus reported benefits.",
+        "directive_followed": "yes"
+      },
+      "latency_s": {
+        "manager_ms": 3875,
+        "ttft_ms": 2909,
+        "main_ms": 4340,
+        "total_ms": 8219
+      }
     }
   ],
   "feedback": [],
@@ -133,17 +167,21 @@
   },
   "upload_errors": [],
   "session_summary": {
-    "n_turns": 2,
-    "n_manager_verdicts": 2,
-    "n_transitions": 0,
+    "n_turns": 3,
+    "n_manager_verdicts": 3,
+    "n_transitions": 1,
     "n_plan_completes": 0,
     "subprobe_coverage": {
       "1": {
         "engaging_default": 2,
         "other": 0
+      },
+      "2": {
+        "engaging_default": 0,
+        "other": 1
       }
     },
-    "last_subprobe_visited": 1,
+    "last_subprobe_visited": 2,
     "total_subprobes_in_plan": 10,
     "outcome": "ran-without-plan-complete"
   }