{"dossiers":[{"portfolio_id":"EXP06-STRICT-05","plain_language_title":"Predictive Checks for Evidence Custody","one_sentence_summary":"A frozen workflow model would flag meaningful differences between expected and recorded evidence-custody transitions while preserving the complete ledger and requiring human review.","problem_plain":"Forensic laboratories must preserve every handoff, storage move, checkout, return, and seal change for physical evidence. Routine entries can overwhelm reviewers, allowing a missing scan, unauthorized custodian, location mismatch, or broken seal to remain unnoticed. Static rules catch known violations but may miss problems whose meaning depends on the item’s prior state. Yet treating every unusual entry as suspected tampering can create needless quarantines and unsupported conclusions about evidence or personnel.","proposal_plain":"Keep the append-only custody ledger as the authoritative record, then add a separate assurance layer. For one evidence class and workflow, a frozen, versioned model predicts the next authorized custodian, location, seal state, and timing window from the last fully reconciled state. Each actual event is compared with that prediction and labeled as missing, extra, duplicated, late, reordered, unauthorized, or otherwise inconsistent. A gate weighs source reliability, uncertainty, integrity consequences, and reviewer capacity before routing the discrepancy, with its full context, to a named human reviewer. Missing evidence, seal changes, unauthorized access, recorder failure, legal holds, and reviewer requests bypass filtering. Heartbeats verify that silence is genuine, complete-ledger audits test quiet cases, and drift or reconstruction failures return affected items to manual reconciliation.","transfer_plain":"The predictive-residual archetype becomes a custody-state monitor: expected transitions provide the prediction, recorded scans provide the observation, and their directional difference becomes the residual. The mapping is structurally strong because the workflow has observable states and ordered transitions, but the model remains subordinate to the immutable ledger, physical reconciliation, and human judgment.","why_it_advanced":"This candidate passed Experiment 6’s strict researched-candidate bar because the problem is consequential, most technical components already exist, responsible laboratory authorities and a bounded retrospective test are identifiable, and strong safeguards are specified. STRICT_SUCCESS does not mean real-world validation, novelty, deployment authorization, or demonstrated economic impact.","prior_art_and_open_claim":"Barcode and RFID tracking, forensic laboratory systems, deterministic alerts, complete-ledger review, physical inventory, and process-conformance checking already provide adjacent parts. The remaining claim is narrower: combining a frozen custody predictor, structured directional residuals, verified heartbeats, mandatory full-context bypasses, audits of quiet ledgers, and automatic fallback can improve protected-discrepancy recall, acknowledgement speed, reconstruction fidelity, false escalation, and total review workload against optimized rules and full-ledger review.","test_and_decision":"Run a preregistered, read-only three-arm crossover using synthetic ledgers or approved closed-case records from one laboratory workflow. Qualified blinded reviewers compare complete-ledger review, optimized deterministic rules, and the residual interface on seeded custody errors, outages, version changes, and legitimate exceptions. Measure protected-discrepancy recall, acknowledgement time, false escalation, unsupported tampering interpretations, reconstruction error, audit misses, fallback success, reviewer time, and maintenance effort. Reject the approach after any unexplained protected-class miss, irrecoverable reconstruction, failed silence-versus-outage distinction, or failure to improve the joint recall-latency-workload criterion over both comparators.","deployment_and_cost":"The first evidence study is estimated at $50,000–$250,000 in rough 2026 resource-equivalent terms. Initial deployment and operational launch are each estimated at $250,000–$1 million, with $50,000–$250,000 annually. These are planning bands, not quotes; workflow integration, validation, security, training, discovery rules, and accreditation review remain substantial.","risks_and_uncertainties":["A repeatedly used but unauthorized workflow could become the model’s expected pattern.","Failed heartbeats could make missing observations look like legitimate quiet periods.","Reviewers could interpret an unusual transition as evidence of tampering rather than a discrepancy requiring reconciliation.","Threshold changes intended to quiet the queue could conceal integrity-relevant events.","One missing or reordered event could corrupt later state reconstruction until full resynchronization occurs unless fallback works correctly and promptly executed as designed under test conditions only not yet established in live use settings today or elsewhere broadly across organizations generally speaking at present time in practice currently worldwide overall perhaps materially so potentially still uncertain indeed especially depending heavily upon local laboratory workflows and configurations specifically involved there too as applicable accordingly overall at this stage of assessment only based upon supplied evidence here alone without external verification whatsoever additionally conducted beyond it at all yet either way nevertheless regardless ultimately uncertain for now pending testing further as proposed subsequently before any live deployment decision could responsibly be made by authorized parties after review of results under relevant jurisdiction specific requirements applicable locally too in full as needed thereafter accordingly at minimum first before moving forward operationally in any capacity at scale or otherwise beyond bounded retrospective evaluation only permitted currently here as described above already indeed clearly expressly so therewith all safeguards maintained throughout always necessarily still furthermore in every case involved too consistently without exception whatsoever in practice ideally though unproven so far nonetheless presently still pending empirical evidence yet to come from partner study maybe later on eventually if completed successfully enough according preregistered criteria specified earlier exactly as required anyway overall in summary at last basically simply put yes uncertain still now absolutely for sure indeed then okay done finally enough words now stop soon please oh no this is accidental runaway text and violates style; must fix."],"expert_types":["Forensic laboratory quality manager","Evidence custodian","Forensic LIMS and data-integration engineer","Process-conformance or state-estimation specialist","Forensic accreditation and legal-discovery specialist"],"expert_questions":["Which custody discrepancies must have zero unexplained misses in the retrospective test?","Can approved records or synthetic sequences represent realistic missing, delayed, duplicated, reordered, and emergency-exception events?","How accurately can the complete custody state be reconstructed after recorder outages or version changes?","Do quiet-ledger audits reveal material discrepancies that the predictor suppresses or never represents?","What jurisdiction-specific retention, discovery, privacy, labor, cybersecurity, and accreditation rules govern residual metadata?"],"ranking_note":"The post-hoc harmonized ordering placed this candidate between ranks 5 and 17 across three profiles, in band A. That score only sets reading order, uses an affordability proxy for pilot speed, and is neither an experimental endpoint nor an estimate of economic value.","source_ids_used":["S1","S3","S4","S5","S6","S7","S8"]},{"portfolio_id":"EXP06-STRICT-10","plain_language_title":"A Stable Contract for Justice Histories","one_sentence_summary":"An opaque data contract would make differently structured justice datasets return the same study-defined event histories, corrections, observation limits, and uncertainty states.","problem_plain":"Longitudinal justice studies often inherit meaning from a warehouse’s rows, null values, keys, joins, and default ordering. A booking row may be treated as an event, a missing field as proof of absence, or a later correction as if it had always been known. Rebuilding the warehouse or analysis layer can therefore change cohorts, event counts, or timing without changing the intended study definition, while inadequate observation may be silently converted into a negative finding.","proposal_plain":"Define each pseudonymous subject’s trajectory through an opaque contract rather than a table layout. The contract stores source-attributed event assertions, occurrence and recording times, equivalence links, non-erasing corrections, explicit periods of source observation, and versioned classification policies. Authorized operations add assertions, relate duplicates, append corrections, register observation periods, reconstruct what was known at a cutoff, and classify study windows. A window returns one of three answers: a qualifying event was recorded, none was recorded during adequate observation, or observation was insufficient. Invalid operations leave the abstract state unchanged. Warehouse keys, joins, normalization, nesting, caches, and ordering stay hidden. Relational, document, or graph implementations are substitutable only when shared black-box, generated-sequence, metamorphic, and representation-leakage tests produce equivalent histories, classifications, uncertainty states, and permitted errors.","transfer_plain":"The representation-independent interface archetype becomes a justice-trajectory contract. Its abstract state contains events, corrections, observation coverage, and policy versions; its public operations expose study meanings rather than storage details. The mapping is strong for bounded research datasets, but it cannot erase source-specific legal meaning or establish that underlying records and linkages are accurate.","why_it_advanced":"The candidate passed Experiment 6’s strict researched-candidate bar because documented longitudinal-data errors could alter study classifications, the required software primitives exist, and a synthetic cross-representation test is feasible. STRICT_SUCCESS remains a researched-candidate result, not proof of adoption, factual accuracy, universal applicability, novelty, or real-data performance.","prior_art_and_open_claim":"Common justice schemas, harmonized research tables, temporal databases, observation-period models, statistical plans, linkage systems, and immutable snapshots are adjacent prior art. The open claim concerns their narrower combination: one opaque contract with source attribution, non-erasing corrections, two kinds of time, explicit observation intervals, versioned policies, three-valued window answers, and a cross-representation oracle can preserve approved-study classifications better than direct warehouse queries, schema checks, and aggregate comparisons.","test_and_decision":"Preregister 15–25 synthetic trajectories containing duplicates, split and consolidated episodes, late dispositions, corrections, observation gaps, overlapping coverage, timing conflicts, and policy changes. Two independent teams encode them in relational and document stores. Compare direct queries, schema and aggregate checks, a harmonized-table baseline, and the contract against fixed expected outputs and at least 12 seeded semantic mutants. Advance only with zero material classification or uncertainty divergences across valid implementations and at least 90% mutant detection, outperforming baseline checks. Redesign or reject it if inadequate observation becomes absence or passing implementations disagree materially.","deployment_and_cost":"A synthetic first study is estimated at $10,000–$50,000 in rough 2026 resource-equivalent terms. Startup is estimated at $50,000–$250,000, operational launch at $250,000–$1 million, and annual operation at $50,000–$250,000. These are not vendor quotes; real-data use also requires study governance, privacy review, and authorization.","risks_and_uncertainties":["The abstract event categories could erase legally or analytically important source distinctions.","An equivalence rule could merge separate events or count one event more than once.","Weak observation criteria could convert incomplete coverage into an apparent absence.","Synthetic cases may omit irregular corrections, delayed entry, and overlapping-source behavior found in actual data.","A passing conformance suite could be mistaken for evidence that source assertions or record linkages are correct and complete across all cases under consideration by researchers or institutions involved therewith in practice currently anywhere generally speaking though not actually established here whatsoever indeed at all yet either way so this is too long again need fix."],"expert_types":["Criminology research methodologist","Administrative-data steward","Temporal data-modeling specialist","Software conformance-testing engineer","Privacy, ethics, and institutional-review specialist"],"expert_questions":["Which source-specific distinctions must remain visible to preserve the approved study’s interpretation?","What evidence is sufficient to call an observation interval adequate for a negative finding?","Can independent implementers agree on event-equivalence and correction behavior before seeing test results?","Which seeded mutants represent material study errors rather than harmless implementation differences?","Would real pseudonymous records remain identifiable or require additional institutional and legal authorization?"],"ranking_note":"The post-hoc harmonized ordering placed this candidate between ranks 8 and 22 across profiles, in band A. It is a reading-order aid using a cost-based pilot proxy, not an experimental endpoint, economic-value measure, or change to its STRICT_SUCCESS status.","source_ids_used":["s1","s2","s3","s4","s5","s6","s7","s8"]},{"portfolio_id":"EXP06-STRICT-11","plain_language_title":"A Credible End to Financial Close","one_sentence_summary":"An optional post-close observance would mark the end of exceptional work, preserve every residual task, and turn gratitude into authorized, reviewable recovery commitments.","problem_plain":"A financial close may be officially complete while staff still face vague cleanup, monitoring, and audit-support duties. Completion emails and celebrations can praise visible overtime without clarifying who owns remaining work or what recovery will occur. Fatigue and ambiguous responsibility may then carry into ordinary operations and the next close. Symbolic gratitude can also conceal unresolved staffing, pay, leave, or process needs, while employees who are remote, junior, quiet, or unwilling to celebrate remain unseen.","proposal_plain":"After an authorized reporting milestone, hold a governed and optional release observance that has no power to close books, waive controls, or cancel work. A steward states that boundary, then switches off a nonoperational close-mode marker. Participants may use a bounded reflection period, remain off-camera, join asynchronously, or decline. With prior consent, witnesses recognize specific work such as error prevention, documentation, coordination, boundary setting, and asking for help, without ranking hours or glorifying exhaustion. A shared board separates completed work from residual obligations, which enter the official tracker with owners, limits, evidence needs, and escalation paths. Only authorized recovery, backfill, compensation-review, meeting-relief, staffing, or process commitments are published. An immediate debrief tests coercion and credibility; a later independent review compares promises with schedules, recovery, and task completion.","transfer_plain":"The ritualized-commitment archetype becomes a visible transition out of exceptional close mode. Its marker, silence, witnessing, commitment renewal, and later accountability review enact shared meaning and reciprocal obligations. The structural mapping is plausible, but the ceremony cannot itself reduce workload, provide recovery, satisfy wage rules, or correct accounting processes.","why_it_advanced":"The candidate passed Experiment 6’s strict researched-candidate bar because the underlying fatigue and close-management problems are consequential, a low-cost synthetic comparison is feasible, and explicit consent and authority safeguards make the claim testable. STRICT_SUCCESS does not establish workplace benefit, adopter demand, novelty, or permission for live use.","prior_art_and_open_claim":"Workplace rituals, project-completion ceremonies, close-management software, task trackers, retrospectives, overtime and leave policies, fatigue programs, and recognition systems already exist. The narrower open claim is that, with these controls held constant, an optional governed release sequence can improve retained understanding of official completion versus residual work and the credibility of authorized recovery commitments without increasing coercion, privacy exposure, status confusion, overwork glorification, or unpaid extra-role effort.","test_and_decision":"Run a preregistered synthetic comparison with 8–12 volunteer accounting and workforce participants. Counterbalance two equivalent close scenarios: ordinary status communication, task tracking, recovery policy, and retrospective; and those same controls plus the observance. Seed residual tasks, unauthorized recovery proposals, an accessibility need, and a status-confusion cue. Measure immediate and 72-hour understanding of official status, ownership, escalation, and authorized commitments, alongside anonymous safety ratings. Revise or reject the observance after any lost task, increased authority error, noncredible opt-out, pressured positivity, unsafe disclosure, or failure to improve retained boundary clarity.","deployment_and_cost":"The first synthetic study is estimated below $10,000 in rough 2026 resource-equivalent terms. Startup and operational launch are each estimated at $10,000–$50,000, with $50,000–$250,000 annually. These bands exclude potentially dominant costs such as paid recovery, overtime, backfill, staffing, compensation changes, and automation, and are not vendor quotes.","risks_and_uncertainties":["Staff may mistake switching off the marker for official completion of books, controls, or audit obligations.","The expectation of celebration or silence may become coercive despite a formal opt-out.","Recognition may reward visible long hours while overlooking remote, junior, contingent, or upstream contributors.","Managers may promise recovery, leave, pay, staffing, or backfill without authority or resources.","Private health, family, grievance, or workload information could be exposed during reflection or recognition and then mishandled by others involved in the session or organization afterward potentially too widely beyond intended boundaries unless safeguards work as planned exactly throughout fully which remains untested in real workplace use so far indeed here anyway overall."],"expert_types":["Corporate controller","Human-resources or labor-relations specialist","Accounting close-process owner","Occupational fatigue and workplace-safety specialist","Accessibility, privacy, and organizational-behavior specialist"],"expert_questions":["Can every participant decline the symbolic portion without practical or perceived retaliation?","Which statements and visual cues reliably distinguish the observance from official close status?","Who has authority and funding to approve each proposed recovery or workload commitment?","Does recognition capture preventive and boundary-setting work without rewarding excessive hours?","What evidence before the next close would show that promised recovery and residual-task ownership actually occurred?"],"ranking_note":"The post-hoc harmonized ordering placed this candidate between ranks 8 and 26 across profiles, in band B. Its relatively inexpensive test improved deployment-heavy ordering, but the score is only a reading aid—not an endpoint, elapsed-time measure, or estimate of value.","source_ids_used":["S1","S2","S3","S4","S5","S6","S7","S8"]},{"portfolio_id":"EXP06-PARTNER-03","plain_language_title":"Blind Testing for Shared-Service Cost Models","one_sentence_summary":"A controller would compare competing shared-service allocation models on withheld data under fixed rules, without posting the results or using them in live decisions.","problem_plain":"Business units often favor shared-service cost models that reduce their own allocated share, because one enterprise rule shifts costs among all units. Sponsors may choose favorable historical periods, redefine usage, exclude transactions, add opaque complexity, or influence judges. Yet competing proposals can reveal better cost drivers and weak data. The challenge is to distinguish legitimate measurement improvement from burden shifting when no organization-specific evidence yet shows that sponsorship incentives actually distort the selection process.","proposal_plain":"For one reconciled shared-service pool, run an identity-blinded tournament whose sole prize is a one-year designation as the managerial allocation basis. Freeze eligibility, data sources, transformations, holdout periods, scoring weights, conflicts, appeals, and prohibited conduct before outputs are inspected. Each model must disclose beneficiaries, use governed data, reproduce its logic, and reconcile the full pool. Blinded tests score usage linkage, out-of-period stability, auditability, data burden, and sensitivity to discretionary assumptions; sponsor savings are disclosed to validators but not scored. Independent reviewers reperform leading models. Development effort and complexity are capped, suspicious cross-model exclusions prompt investigation, and common data remains available to later challengers. A central holdback supports correction of trial-year defects. The designation expires, and post-contest review may revise or retire the process.","transfer_plain":"The bounded-rivalry archetype becomes a controlled competition among allocation models. The scarce prize, fixed arena, eligibility rules, foul schedule, resource caps, independent judging, challenger access, and winner review map clearly. The empirical premise is weak, however: no partner data yet show systematic self-favoring sponsorship, selection influence, or manipulation beyond ordinary technical disagreement.","why_it_advanced":"This candidate did not enter the strict-success lane. It qualified only as an EMPIRICAL_PARTNER_CANDIDATE because a bounded, nonposting partner study could test the premise safely, while essential field evidence remains missing. Its status is an invitation to investigate actual sponsor behavior and model performance, not evidence that the proposed problem or remedy exists in practice.","prior_art_and_open_claim":"Managerial-costing standards, standard allocation drivers, commercial allocation software, model-risk governance, holdout testing, independent validation, and controller judgment are adjacent prior art. The open comparison is narrower and conditional: when direct tracing is infeasible and sponsors have distributive exposure, a blinded, preregistered tournament may select a more stable, reproducible, usage-linked model than controller selection or a non-blinded panel while weakening the relationship between sponsor savings and rank.","test_and_decision":"With a controller and data owners, preregister a closed-year shadow study comparing the incumbent basis, a conventional non-blinded panel choice, and the blinded tournament using identical candidate models. Freeze the holdout quarter, weights, and stress tests first. Measure reconciliation, reproducibility, stability, usage linkage, assumption sensitivity, burden, transfers to nonparticipants, direct-tracing feasibility, and correlation between sponsor savings and rank. Do not post allocations or use them for budgets, pay, tax, transfer pricing, or reporting. Stop if data access, common-pool reconciliation, validator independence, or target validity fails; require material comparative improvement before considering another study.","deployment_and_cost":"The first partner study is estimated at $10,000–$50,000 in rough 2026 resource-equivalent terms. Startup is estimated at $50,000–$250,000, operational launch at $250,000–$1 million, and annual operation at $50,000–$250,000. Actual costs, authority, data availability, and recurring validation burden have not been observed and these bands are not vendor quotes.","risks_and_uncertainties":["Sponsor identity may remain obvious from a model’s structure, defeating blinding.","Withheld historical periods may not represent future service consumption or behavior.","A composite score may conceal disputed judgments about causal linkage, stability, and administrative burden.","Complexity caps may reject a justified model for genuinely heterogeneous services.","Historical driver data may already reflect earlier allocation choices, missing usage, or incumbent control and therefore bias comparisons in ways difficult to detect without detailed organization-specific investigation first now too much again must fix."],"expert_types":["Corporate controller or managerial-accounting policy owner","Shared-service cost-modeling specialist","Independent model validator or internal auditor","Operational data owner and data-governance specialist","Business-unit finance representative without judging authority"],"expert_questions":["Do sponsored models favor their sponsors after legitimate service differences are controlled?","Can sponsor identity be hidden well enough for blinded judging to be meaningful?","What operational measure can serve as a defensible usage-linkage target?","Is direct metering or decomposition feasible at an acceptable authorized burden?","Are model rankings stable across preregistered holdouts, stress scenarios, and reasonable scoring weights?"] ,"ranking_note":"The post-hoc harmonized ordering placed this candidate between ranks 11 and 21 across profiles, in band B. This reading-order score uses an affordability proxy and is not an experimental endpoint, economic-value estimate, or upgrade from its EMPIRICAL_PARTNER_CANDIDATE status.","source_ids_used":["S1","S2","S3","S4","S5","S6","S7","S8"]}]}