{"schema_version":1,"research_id":"eoa_inverse_innovation_exp06_external_evaluation_20260803","source_assessment_id":"predictive_residual_processing__human_computer_interaction:P3:v0","cell_id":"predictive_residual_processing__human_computer_interaction","search_queries":["bulk edit preview changes before apply product dry run records","bulk action confirmation dialog preview affected records usability research","GitHub bulk update preview changes dry run","Atlassian bulk edit preview changes issues confirmation","site:w3.org WCAG confirmation data deletion legal financial transactions error prevention","site:developer.hashicorp.com terraform plan preview changes saved plan stale state","research confirmation dialog bulk operation errors user study","visual diff information overload change detection study interface","official product documentation bulk edit preview before apply every record","bulk update dry run preview before apply SaaS first party","site:learn.microsoft.com bulk edit preview changes records","site:support.airtable.com bulk update preview records","\"Supporting dynamic change detection: using the right tool for the task\"","\"Supporting dynamic change detection\" CHEX study","site:frontiersin.org Supporting dynamic change detection using the right tool for the task","Europe PMC PMC5256471 Supporting dynamic change detection","PubMed Supporting dynamic change detection right tool task Vallieres 2016"],"sources":[{"source_id":"S1","title":"Understanding Success Criterion 3.3.4: Error Prevention (Legal, Financial, Data)","publisher":"World Wide Web Consortium (W3C), Web Accessibility Initiative","url":"https://www.w3.org/WAI/WCAG20/Understanding/error-prevention-legal-financial-data","source_class":"STANDARD","publication_date":"n.d.","accessed_at":"2026-08-03","claims_supported":["Modification or deletion of user-controllable stored data can have serious consequences.","Review-and-correct, confirmation, or reversibility are recognized safeguards before consequential actions.","A residual preview cannot replace an accessible confirmation or recovery mechanism where WCAG 3.3.4 applies."]},{"source_id":"S2","title":"Supporting dynamic change detection: using the right tool for the task","publisher":"Cognitive Research: Principles and Implications / Springer Nature","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC5256471/","source_class":"PRIMARY_RESEARCH","publication_date":"2016-12-19","accessed_at":"2026-08-03","claims_supported":["In a controlled multitasking simulation, participants missed material display changes.","An explicit change-history aid did not improve critical-change detection in the multitasking condition and increased workload; benefits appeared only in a simpler change-detection task.","Attention-focused change displays require task-context testing and can worsen performance, directly supporting the proposal's intervention falsifier."]},{"source_id":"S3","title":"Bulk Ops for Jira","publisher":"Magrathea Software via Atlassian Marketplace","url":"https://marketplace.atlassian.com/apps/3844951205/%26","source_class":"COMMERCIAL_FIRST_PARTY","publication_date":"2026-06-23","accessed_at":"2026-08-03","claims_supported":["A shipping paid Jira product supports bulk operations beyond 1,000 issues.","Its mandatory pre-run preview identifies the field and displays before-and-after values for every matched issue.","It provides match preview, explicit confirmation, per-issue execution status, audit trail, CSV export, and admin-only access."]},{"source_id":"S4","title":"Preview a batch of update sets","publisher":"ServiceNow","url":"https://www.servicenow.com/docs/r/application-development/system-update-sets/us-hier-preview.html","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","publication_date":"2026-03-12","accessed_at":"2026-08-03","claims_supported":["ServiceNow administrators can preview an entire batch of update sets before applying it.","The workflow foregrounds preview problems, supports resolving them, and reruns the preview.","Batch-level exception review before change execution is already an established adjacent product practice."]},{"source_id":"S5","title":"Bulk Update Preview","publisher":"Stibo Systems","url":"https://doc.stibosystems.com/doc/version/latest/web/content/bulkupd/createbu/bulk_update_preview.html","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","publication_date":"n.d.; page identifies 2025.3 Update","accessed_at":"2026-08-03","claims_supported":["A bulk-update wizard previews old and prospective new values before execution.","The interface samples at most ten objects rather than showing the whole set.","Warnings and errors are emphasized and can be resolved by returning to operation configuration, closely approximating sample-plus-exception presentation."]},{"source_id":"S6","title":"terraform plan command reference","publisher":"HashiCorp","url":"https://developer.hashicorp.com/terraform/cli/commands/plan","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","publication_date":"n.d.","accessed_at":"2026-08-03","claims_supported":["A noncommitting execution plan can refresh current state, compare it with declared configuration, and propose prospective changes for review.","Disabling refresh can yield an incomplete or incorrect plan, supporting snapshot-freshness requirements.","Saved plans may contain sensitive values, supporting strict preview-data handling and retention controls."]},{"source_id":"S7","title":"Journey Dry run","publisher":"Adobe Experience League","url":"https://experienceleague.adobe.com/en/docs/journey-optimizer/using/orchestrate-journeys/create-journey/journey-dry-run","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","publication_date":"n.d.","accessed_at":"2026-08-03","claims_supported":["Adobe Journey Optimizer runs draft journeys against production data without sending communications or updating profiles.","Dry-run events carry identifiers and can be monitored and exported.","Start and stop authority is permission-restricted, and documented simulation differences show why a dry run is not necessarily identical to live execution."]},{"source_id":"S8","title":"Using the Dry run to validate and apply sync changes","publisher":"Speakap","url":"https://support.speakap.com/hc/en-us/articles/27720272288540-Using-the-Dry-run-to-validate-and-apply-sync-changes","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","publication_date":"2026-05-26","accessed_at":"2026-08-03","claims_supported":["Speakap requires dry runs before HR mapping and user-sync changes become effective.","Results include summaries, warnings, mutation counts, highlighted state differences, raw payload access, and expandable full user and group details.","The product explicitly seeks to prevent unintended mutations and requires verification, numerical confirmation, and final execution, demonstrating an identifiable adopter and expressed need."]}],"problem_evidence":{"support":"MODERATE","rationale":"W3C recognizes serious consequences from erroneous stored-data modification and the value of review before finalization (S1). Existing vendors devote mandatory previews, warnings, confirmation, and audit facilities to large bulk changes (S3, S5, S8), making the operational concern visible. Primary HCI evidence shows that material changes can be missed under multitasking and that an unfiltered change aid can add workload (S2). However, no located study measures the prevalence or loss attributable specifically to exceptional effects buried inside administrative bulk-action previews.","source_ids":["S1","S2","S3","S5","S8"]},"stakeholder_evidence":{"support":"MODERATE","rationale":"Speakap is an identifiable adopter whose documentation calls dry run its critical safety feature and requires it before user-sync mutations (S8). Magrathea, ServiceNow, Stibo, HashiCorp, and Adobe have implemented related preview or dry-run workflows (S3-S7), while W3C supplies an authorizing accessibility rationale for review safeguards (S1). This establishes demand for safe previews, but no source expresses demand for the proposal's specific model-relative residual ranking, independent raw audit sampling, or predictor/executor separation.","source_ids":["S1","S3","S4","S5","S6","S7","S8"]},"prior_art":{"proximity":"SUBSTANTIAL_COLLISION","closest_analogues":[{"name":"Speakap User Sync dry run","similarity":"Mandatory pre-apply simulation over real organizational records; presents summaries, warnings, mutation counts, specific differences, raw payloads, expandable complete details, permissions, and confirmation.","remaining_difference":"It compares source rules/data with current state rather than comparing a separately predicted effect contract with an independently executed dry run; it does not document random suppressed-row audits, consequence-weighted residual ranking, model checksums, or residual-driven model governance.","source_ids":["S8"]},{"name":"Stibo Bulk Update Preview","similarity":"Shows prospective old/new record states, limits the ordinary sample to ten objects, and foregrounds warnings and errors before a bulk update.","remaining_difference":"The documented selection is a fixed small sample plus rule warnings, not a reconstructible residual channel formed from independent expected and simulated effects; no raw-audit or drift protocol is documented.","source_ids":["S5"]},{"name":"ServiceNow batch update-set preview","similarity":"Previews a full batch, foregrounds detected problems, supports remediation, and reruns the preview before application.","remaining_difference":"The problem list is an established compatibility/validation workflow, not an explicit record-level predictor-versus-dry-run residual with uncertainty, sampling, reconstruction, and fallback metrics.","source_ids":["S4"]},{"name":"Bulk Ops for Jira","similarity":"Provides exact-scope preview, mandatory confirmation, every-record before/after display, large-batch execution, and per-record audit trails.","remaining_difference":"It is the proposal's full-diff comparator rather than residual compression; it does not prioritize unexplained deviations against a versioned operation-effect model.","source_ids":["S3"]},{"name":"Terraform plan","similarity":"Uses current state and a declared target to generate noncommitting prospective changes, supports review before application, and recognizes stale-state and sensitive-plan hazards.","remaining_difference":"The execution plan itself is the prospective change representation; it is not compared with a second independent dry-run executor to isolate unexpected execution heterogeneity, nor is the UI centered on sampled expected rows plus risk-weighted residuals.","source_ids":["S6"]}],"distinctive_claim_remaining":"For one reversible bulk metadata edit, an interface that compares a versioned contract-derived expected effect with an independently computed dry-run effect, then displays protected effects, a random full-row sample, and consequence-weighted residuals with persistent full-preview fallback, will reduce median review time by at least 20% while remaining noninferior to a complete before/after preview by no more than 5 percentage points in detection of injected material effects and uniformly wrong scope, with zero compressed protected effects and exact held-out reconstruction.","confidence":"HIGH"},"implementation_evidence":{"support":"MODERATE","rationale":"Commercial systems establish feasibility for large-batch previews, samples, warnings, per-record details, audit trails, confirmations, and role restrictions (S3-S5, S8). Terraform and Adobe establish versioned noncommitting plans or simulations, state refresh, event identifiers, monitoring, and permission controls (S6-S7). The unverified engineering burden is the proposal's crucial independence requirement: predictor and dry-run implementations may share semantics or code, simulated and live behavior can differ, and prospective effects may include unknowable external side effects. Consequently, shadow implementation is credible for one reversible operation, but production correctness is not established.","source_ids":["S3","S4","S5","S6","S7","S8"]},"scores":{"meaningful_impact":{"score":4,"rationale":"Erroneous modification or deletion can have serious consequences, and multiple products treat dry-run review as a safety control; scoped impact magnitude remains unmeasured.","source_ids":["S1","S8"]},"stakeholder_pull":{"score":4,"rationale":"Several identifiable vendors have implemented mandatory preview or dry-run workflows, with Speakap explicitly describing mutation prevention as the purpose.","source_ids":["S3","S4","S5","S8"]},"incremental_advantage":{"score":2,"rationale":"Sampling plus highlighted warnings and expandable raw detail already exist, while primary research shows that an added change aid can increase workload. Advantage over these comparators requires live user testing.","source_ids":["S2","S5","S8"]},"distinctiveness_plausibility":{"score":3,"rationale":"The predictor-versus-independent-dry-run residual, raw random audit, reconstruction test, and forced decompression form a testable remaining contrast, but the user-visible workflow substantially overlaps existing exception-oriented previews.","source_ids":["S4","S5","S8"]},"technical_implementability":{"score":3,"rationale":"Plans, dry runs, snapshots, before/after structures, warnings, and audit trails are established. Independence from commit semantics and faithful simulation of external effects remain material challenges.","source_ids":["S3","S5","S6","S7","S8"]},"adoption_authority_feasibility":{"score":4,"rationale":"Existing products assign preview and execution to administrators or permissioned publishers, and a shadow-only reversible-operation evaluation requires no automated commitment authority.","source_ids":["S3","S4","S7","S8"]},"evidence_readiness":{"score":4,"rationale":"The candidate supplies explicit comparators, injected failures, observable logs, and falsifiers; a scripted shadow study can be bounded tightly, though no prototype or partner is evidenced.","source_ids":["S2","S3","S5","S8"]},"safety_net_benefit":{"score":4,"rationale":"Full preview, raw samples, explicit confirmation, stale-state checks, permissions, and reversible scope are supported by standards and analogous products; their integrated reliability is untested.","source_ids":["S1","S5","S6","S7","S8"]},"scalability":{"score":3,"rationale":"Products demonstrate previews beyond 1,000 records and large-batch workflows, but per-operation contracts, independent execution semantics, audit samples, and governance create recurring integration cost.","source_ids":["S3","S4","S8"]}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"Build a shadow prototype for one reversible metadata-edit operation; create scripted heterogeneous records and three preview conditions; conduct a counterbalanced study with approximately 24-40 representative administrators; analyze detection, workload, review time, reconstruction, and fallback use.","confidence":"MODERATE","assumptions":["Existing bulk-action and dry-run APIs are available in a test environment.","No production writes are enabled.","Cost includes product engineering, UX research, security review, participant time, and analysis.","The predictor is initially rule-based rather than a trained high-complexity model."],"source_ids":["S2","S3","S5","S8"]},"initial_deployment_startup":{"band_2026_usd":"250K_TO_1M","scope":"Productionize one operation with immutable snapshots, versioned contracts, independently checked dry-run output, structured diffs, protected classes, audit sampling, complete-preview fallback, telemetry, accessibility, privacy review, and rollback integration.","confidence":"LOW","assumptions":["The host product already has transactional bulk edits, authorization, audit logging, and recovery.","Only one schema-stable reversible operation is launched.","External side effects are excluded or forced to full preview.","Estimate is resource-equivalent and not a vendor quote."],"source_ids":["S1","S3","S6","S8"]},"operational_launch":{"band_2026_usd":"1M_TO_5M","scope":"Extend to several heterogeneous bulk-action families; establish contract ownership, operation-specific consequence tables, concurrency handling, load testing, accessibility validation, security/compliance approval, incident playbooks, training, staged rollout, and controlled experiments.","confidence":"LOW","assumptions":["Three to six operation families across one mature SaaS product.","Deletion, finance, credentials, ownership, and irreversible actions remain excluded initially.","Independent semantic checks need bespoke work per action family.","Estimate is resource-equivalent and not market-size evidence."],"source_ids":["S1","S3","S6","S7","S8"]},"annual_recurring":{"band_2026_usd":"250K_TO_1M","scope":"Maintain contracts and schemas, monitor residual distributions and fallback rates, review material misses, audit random raw samples, retest protected classes, handle incidents, and support user research and accessibility reviews.","confidence":"LOW","assumptions":["One product and several operation families.","A small cross-functional ownership rotation plus periodic research and audit work.","Storage and compute remain secondary to engineering and governance labor.","Estimate excludes organization-wide licensing and realized-loss savings."],"source_ids":["S1","S3","S6","S7","S8"]}},"verified_pipeline_gates":{"externally_supported_problem":{"status":"YES","reason":"Serious stored-data mistakes, missed visual changes under workload, and multiple mandatory bulk-preview products externally support the underlying problem, although domain prevalence is unknown.","source_ids":["S1","S2","S3","S5","S8"]},"externally_credible_adopter_or_authorizer":{"status":"YES","reason":"Speakap, ServiceNow, Stibo, Magrathea, HashiCorp, and Adobe are credible adopters of adjacent pre-commit preview or dry-run controls; W3C provides relevant review-and-correction authority.","source_ids":["S1","S3","S4","S5","S6","S7","S8"]},"distinct_testable_incremental_claim":{"status":"YES","reason":"A residual interface can be compared directly with complete-diff and sample-plus-warning previews using predeclared detection, time, workload, reconstruction, protected-effect, and fallback outcomes.","source_ids":["S2","S3","S5","S8"]},"bounded_next_evidence_step":{"status":"YES","reason":"One reversible operation, immutable scripted data, disabled commits, injected failure classes, three comparators, and explicit stopping thresholds bound the experiment.","source_ids":["S2","S3","S5","S8"]},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The proposed next step is shadow-only with no commits; protected effects are fully displayed, the complete preview remains available, and the permissioned user retains authority. This gate does not authorize production deployment.","source_ids":["S1","S7","S8"]},"credible_cost_scope_and_range":{"status":"UNCERTAIN","reason":"The four bands are scoped resource-equivalent estimates grounded in the observed feature set, but no direct pricing, staffing, integration-complexity, or host-system evidence was located.","source_ids":["S3","S5","S6","S7","S8"]}},"next_evidence_step":"Pre-register and run a shadow-only, counterbalanced within-subject study for one reversible metadata-edit operation with approximately 24-40 representative administrators. Compare (A) complete per-record before/after preview, (B) fixed sample plus static warnings, and (C) the proposed protected-plus-random-sample-plus-ranked-residual preview. Use the same immutable snapshot, operation, commit-disabled control, and recovery plan. Inject skipped records, type coercion, null propagation, dependency changes, out-of-scope selection, stale snapshot, checksum mismatch, uniformly wrong operation, and a protected permission effect. Primary outcomes are detection and correct classification of material effects and wrong scope; secondary outcomes are review time, workload, full-preview requests, reconstruction disagreement, and total model-plus-review effort. Falsify the scoped claim if residual preview is more than 5 percentage points worse than complete preview on either detection outcome, fails to reduce median review time by at least 20%, compresses any protected effect, produces any held-out reconstruction mismatch, or lets a stale/mismatched preview proceed without fallback.","blocking_evidence":["No field evidence shows that exceptional effects are routinely buried in current administrative bulk previews or quantifies resulting harm.","No partner, budget holder, or product owner has expressed demand for model-relative residual ranking specifically.","No usability result establishes noninferior detection of both execution heterogeneity and uniformly wrong scope.","No implementation demonstrates sufficient semantic independence between the expected-effect predictor, dry-run executor, and eventual commit path.","No evidence establishes safe handling of concurrent edits, unknowable external side effects, or sensitive outlier salience.","Cost bands lack direct staffing, vendor-pricing, and integration-complexity observations.","World novelty, patentability, freedom to operate, market size, and realized impact remain unmeasured."],"research_disposition":"PARTNERED_RESEARCH_PROGRAM","world_novelty_boundary":"Bounded search found substantial collisions in mandatory full-diff previews, sampled old/new previews with warnings, batch problem previews, state-to-plan diffs, and dry runs with summaries, raw detail, permissions, and confirmation. It did not establish whether the complete governed combination—contract-derived expected effects compared with an independently executed dry run, consequence-weighted residual presentation, random suppressed-row audits, reconstruction checks, reviewed model updates, and forced full-preview fallback—has been implemented or published. World novelty, patentability, freedom to operate, market size, and realized impact are explicitly unmeasured.","arm":"COMPLETE_PROPOSAL_PORTFOLIO","candidate_version":0,"controller_recommendation":{"action":"STOP_EMPIRICAL_RESEARCH_NEEDED","repairable":false,"material_progress_observed":true,"progress_targets":["Secure a bulk-administration product partner with a reversible metadata operation and representative evaluators.","Demonstrate that predictor, dry-run, and commit semantics have independently testable checks rather than one correlated implementation.","Pre-register the three-condition comparison, 5-percentage-point detection noninferiority margin, 20% median review-time target, zero protected-effect compression rule, and exact reconstruction requirement.","Show noninferior detection of injected material effects and uniformly wrong scope while meeting the review-time target.","Document every protected case, a random sample of suppressed rows, stale-state and checksum fallbacks, workload, full-preview use, and total maintenance effort.","Replace inferential cost bands with observed engineering, research, audit, storage, and governance effort."],"reason":"Bounded web research verifies the underlying safety concern, credible adopters, technical analogues, and substantial prior-art overlap, but cannot determine the proposal's incremental HCI advantage or correlated-blind-spot safety. Those questions require a prototype, representative users, proprietary workflow access, and controlled live testing. Under the stated controller rule this requires STOP_EMPIRICAL_RESEARCH_NEEDED, with repairable set to false."},"proposal_index":3}