{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp06_four_proposal_generalization60_20260803","cell_id":"predictive_residual_processing__religious_studies_theology","arm":"COMPLETE_PROPOSAL_PORTFOLIO","candidate_id":"prp-formative-hermeneutics-residual-review-004","proposal_index":4,"version":0,"title":"Predictive-Residual Formative Review for Interpretive Coursework","problem":"In a religious-studies or theology course with recurring low-stakes interpretive assignments, instructors must repeatedly verify expected competencies—such as accurate source attribution, distinction between descriptive and normative claims, tradition-specific framing, and evidence-to-claim linkage—while also detecting subtle new misconceptions, unsupported transfers, or unexpectedly successful reasoning. Full manual review preserves context but assigns equal attention to repeated mastery and informative change. Fixed rubrics or automated flags can reduce effort but may suppress valid alternative interpretations, encode a dominant tradition as normal, or mistake personal belief for academic performance.","actors":["Students completing low-stakes formative assignments","Course instructor","Teaching assistants or formative-feedback reviewers","Curriculum or program faculty defining academic learning targets","Accessibility and student-data stewards","Tradition- or language-literate advisors consulted for disputed interpretations"],"observable_state":"Across a declared sequence of low-stakes assignments, the course can observe each student's provenance-linked rubric profile, the versioned prediction made before the next response, the actual submitted response, structured residuals, instructor validations, full-response expansions, student challenges, model-version mismatches, fallback events, and later performance on the same skill. The relevant problem is measurable through review time, repeated confirmation of already demonstrated components, delayed feedback, reconstruction disagreement, missed valid alternatives, residual calibration, and independent full-marking audits. The model does not observe or infer the student's faith, sincerity, spiritual standing, or private identity.","consequence":"When routine rubric verification consumes most formative-review capacity, feedback on emerging reasoning changes may arrive late or remain generic. If predictive filtering is poorly governed, it can instead reinforce an instructor's prior view of a student, overlook a valid but unexpected interpretation, or systematically misread work grounded in less represented traditions, languages, or argumentative styles. Either failure can weaken formative feedback and make academic evaluation less contestable.","affected_objective":"Focus bounded instructor attention on informative changes in students' academic reasoning while preserving complete submissions, interpretive plurality, transparent learning targets, equitable review, student contestability, and human control over all consequential assessment.","intervention":"Create a shadow-mode formative-review workspace for one bounded assignment sequence. For each consenting student, a versioned learner-state model records uncertainty-bearing evidence about explicitly taught academic skills, using only prior work from the same course and task family. Before the next low-stakes response is opened, the model predicts the rubric-level features likely to appear, such as source attribution, emic-versus-etic distinction, qualification of comparisons, evidence linkage, and recognition of internal diversity. It does not predict exact wording, doctrinal correctness, personal belief, or a summative grade. The untouched submission is then coded against the same declared features. A structured comparator produces signed residuals: unexpected improvement, omitted evidence, repeated conflation, changed qualification, unsupported inference, novel but defensible interpretation, coding disagreement, or out-of-scope response. A precision-and-consequence gate prioritizes residuals using model uncertainty, source clarity, rubric consequence, reviewer agreement, accessibility context, and risk of suppressing a valid alternative. Expected components may be collapsed only in the instructor's first-pass interface; the matching model version plus residuals reconstructs the complete rubric profile, and the complete submission remains one action away. An instructor or teaching assistant must validate every residual before feedback or model updating. Validated residuals revise the learner-state estimate and select a bounded feedback action, while structural changes to the rubric or model occur only after periodic review. Random responses, risk-stratified responses, all valid-alternative flags, and all student challenges receive independent full marking. High-stakes assessments, personal testimony, accommodation-sensitive work, low-confidence predictions, new task genres, disputed cultural or linguistic interpretations, and insufficiently represented response types bypass residual review. Drift, audit disagreement, version mismatch, missing work, or error-budget breach restores complete manual review.","structural_mapping":[{"archetype_element":"Prediction target definition","domain_realization":"Predict the presence, absence, and confidence of declared academic reasoning features in a student's next low-stakes response within one course and task family."},{"archetype_element":"Generative model state","domain_realization":"A versioned, student-specific and uncertainty-bearing learner state derived from prior consented coursework, with no variables for religious identity, sincerity, or spiritual status."},{"archetype_element":"Model scope and horizon","domain_realization":"The predictor is limited to named formative skills, the current course, a declared assignment family, and the next response; it cannot transfer automatically across courses or assessment purposes."},{"archetype_element":"Expected behavior","domain_realization":"A pre-response rubric profile specifying which taught reasoning features are expected and how uncertain each expectation is."},{"archetype_element":"Actual behavior","domain_realization":"The complete student submission and a provenance-linked human coding of its reasoning features, alternative interpretations, and uncertainties."},{"archetype_element":"Prediction comparator","domain_realization":"A structured comparison preserving direction and type: improvement, omission, recurrence, qualification change, unsupported transfer, valid alternative, coding dispute, or out-of-scope response."},{"archetype_element":"Prediction-error signal","domain_realization":"A residual review item containing the changed feature, relevant evidence span, predicted and observed states, uncertainty, model version, and candidate feedback action."},{"archetype_element":"Precision-weighting rule","domain_realization":"Residual priority combines model uncertainty, evidence clarity, academic consequence, reviewer agreement, accessibility context, underrepresented interpretation risk, and review cost."},{"archetype_element":"Residual propagation channel","domain_realization":"A bounded instructor queue foregrounds validated changes and sends each accepted residual to a defined feedback, clarification, or full-review action."},{"archetype_element":"Reconstruction and synchronization","domain_realization":"The workspace reconstructs the complete rubric profile only from the compatible learner-model version plus residuals; every profile links directly to the untouched response."},{"archetype_element":"Update rule","domain_realization":"Only human-validated residuals revise the learner-state estimate; fast evidence updates are separated from slower rubric, threshold, and model revisions."},{"archetype_element":"Freshness and drift control","domain_realization":"A new genre, syllabus change, long assignment gap, accommodation change, residual-distribution shift, or repeated audit disagreement invalidates prior predictions."},{"archetype_element":"Residual error budget","domain_realization":"Predeclared tolerances govern reconstruction disagreement, suppressed valid alternatives, unreviewed residual mass, subgroup error, and cumulative uncertainty; mandatory-bypass misses have zero tolerance."},{"archetype_element":"Raw-signal audit sample","domain_realization":"Independent graders mark random and risk-stratified complete submissions without seeing the prediction or residual queue, then compare findings with the reconstructed profile."},{"archetype_element":"Safety-critical bypass and fallback","domain_realization":"High-stakes work, personal testimony, accommodation-sensitive work, disputed cultural or linguistic interpretation, student challenge, and low-confidence cases receive complete human review."},{"archetype_element":"Attention and bandwidth budget","domain_realization":"The scarce resource is instructor formative-feedback time, counted together with coding, model maintenance, audit, contestation, and fallback costs."}],"mechanism_mapping":[{"mechanism_slug":"forecast_backtesting","role":"Walk-forward testing on earlier assignment sequences determines whether each skill and task family is predictable enough for shadow-mode residual review.","counterfactual_removal":"Without temporally held-out testing, prior student work could be used to justify suppression without evidence that the prediction remains valid on the next task."},{"mechanism_slug":"predictive_codec","role":"The instructor interface represents the expected rubric profile implicitly and supplies structured residuals from which the complete current profile is reconstructed.","counterfactual_removal":"Without expectation-plus-correction reconstruction, the system becomes an alert generator rather than a predictive residual representation."},{"mechanism_slug":"innovation_residual_filter","role":"The learner-state estimate carries uncertainty and uses the gap between predicted and observed reasoning features to adjust the current estimate without treating one response as definitive.","counterfactual_removal":"Without uncertainty-weighted innovation, a single unusual answer could overwrite the learner model or be dismissed because it conflicts with an established expectation."},{"mechanism_slug":"temporal_difference_update","role":"The signed difference between expected and observed skill evidence provides a bounded teaching signal across sequential assignments and helps identify whether earlier feedback transferred to the next task.","counterfactual_removal":"Without a sequential update path, residuals would describe isolated surprises but would not test or revise expectations about developing academic reasoning."},{"mechanism_slug":"confidence_threshold_table","role":"A versioned table maps confidence, consequence, task type, accessibility status, and interpretation risk to collapse, full review, second review, or fallback.","counterfactual_removal":"Without explicit thresholds, reviewers could quietly change suppression policy to manage workload rather than a declared fidelity and equity budget."},{"mechanism_slug":"model_version_checksum_handshake","role":"It verifies that prediction, residual, reconstruction, rubric, syllabus state, and instructor interface use compatible versions before any item is collapsed.","counterfactual_removal":"Without version compatibility, a response could be reconstructed against an obsolete rubric or learner state and still appear internally coherent."},{"mechanism_slug":"prediction_error_review","role":"Instructors periodically classify material misses as learner-state, rubric, coding, task-design, accessibility, cultural-context, or model-scope errors before authorizing changes.","counterfactual_removal":"Without structured review, residuals may generate feedback while recurring model or assignment defects remain uncorrected."},{"mechanism_slug":"shadow_raw_channel_sampling","role":"Independent full marking of random and risk-stratified submissions measures what the residual interface suppressed, including valid alternative interpretations.","counterfactual_removal":"Without an independent full-response path, the model and its rubric coding would evaluate their own blind spots."},{"mechanism_slug":"raw_signal_fallback_switch","role":"It restores complete manual review when prediction is stale, uncertain, disputed, accommodation-sensitive, high-stakes, mismatched, or outside the tested task family.","counterfactual_removal":"Without fallback, students would remain subject to a compressed review precisely when the learner model is least trustworthy."},{"mechanism_slug":"surprise_to_action_bridge","role":"Each human-validated residual maps to a specific formative action: acknowledge improvement, request evidence, clarify a distinction, offer an alternative source, schedule discussion, or conduct full review.","counterfactual_removal":"Without a defined feedback action and owner, residuals could populate a dashboard without changing the student's learning opportunity."}],"causal_chain":["Declared academic reasoning skills and a bounded assignment family define the prediction target.","Prior consented responses produce a versioned, uncertainty-bearing learner state for each student.","Before the next response is opened, the model predicts its rubric-level feature profile.","Human coding of the complete response supplies the observed feature profile and preserves valid alternatives and disagreement.","The comparator isolates model-relative changes rather than retransmitting every expected rubric confirmation to the first-pass review queue.","Precision, consequence, accessibility, and interpretation-risk weighting determine which residuals receive immediate instructor attention and which cases bypass compression.","The compatible learner state plus residuals reconstructs the complete rubric profile, while the instructor can open the full submission before acting.","Human-validated residuals produce specific formative feedback and bounded learner-state updates.","Independent full marking tests reconstruction fidelity, valid-alternative coverage, and systematic errors; failures trigger complete-review fallback or model retirement.","The loop continues only if any saved first-pass attention exceeds model, audit, challenge, and fallback costs without breaching fidelity, equity, or authority constraints."],"baseline":"The comparator condition is complete manual rubric review of every low-stakes response by an instructor or teaching assistant, with the same submissions, learning targets, source access, accommodations, feedback options, and review personnel but without a predictive learner state, residual queue, or collapsed expected components.","nearest_rivals":["A fixed analytic rubric, which makes criteria explicit but applies the full checklist to every response and does not maintain a versioned expectation-plus-residual learning loop.","Automated essay scoring or feedback, which may classify a complete response but does not require reconstructive residual encoding, independent raw audits, model synchronization, or human validation before action.","A mastery dashboard, which summarizes accumulated scores but does not predict the next response, preserve structured signed errors, or use full-response fallback when its model fails.","Adaptive quizzes, which select subsequent questions from estimated mastery but do not compress instructor review of open interpretive work into reconstructable residuals.","Instructor sampling of student work, which reduces workload but leaves unsampled responses unreconstructable and does not use validated errors to update an explicit learner model.","Comment banks or reusable feedback templates, which reduce writing effort but do not identify model-relative changes or audit what an expectation suppressed."],"remaining_contrastive_claim":"The proposal's testable distinction is the combined architecture of a pre-response learner prediction, structured reasoning residuals, uncertainty-and-consequence routing, model-compatible rubric reconstruction, human-validated sequential updating, independent full-response audits, and mandatory fallback. If it merely automates grading, summarizes mastery, or reuses comments without this reconstructive and corrective loop, it does not instantiate the proposal.","authority_safety":{"decision_authority":"Faculty retain authority over learning targets and course design; the instructor retains authority over formative feedback; students may inspect and challenge their reconstructed profiles and control optional personal disclosures; accessibility personnel govern accommodation-relevant handling. The predictive system has no grading, disciplinary, admissions, ordination, employment, or pastoral authority.","authorized_first_step":"Run a read-only retrospective shadow evaluation on de-identified, already authorized low-stakes coursework. Do not expose residual rankings to students or original graders, change any recorded grade, update a live student profile, or alter course progression.","excluded_actions":["No summative grading, rank ordering, pass-fail decision, admissions action, discipline, employment action, ordination decision, or pastoral judgment","No inference or scoring of faith, belief, sincerity, spirituality, orthodoxy, identity, character, or community membership","No universal model of a correct religious interpretation where the curriculum permits reasoned alternatives","No cross-course, cross-institution, or future-use transfer of a learner model without separate authorization and student notice","No compression of personal testimony, trauma-related material, accommodation-sensitive work, student challenges, or high-stakes assessment","No automatic feedback or model update without human validation","No lowering of audit, fallback, or valid-alternative protections to meet marking-volume or turnaround targets","No replacement of instructor responsibility for reading complete work whenever context is material"],"halt_rollback":"Immediately disable residual presentation for an affected student, task, or course upon a privacy incident, mandatory-bypass miss, rubric-version mismatch, unsupported inference, repeated reconstruction failure, systematic error associated with language or interpretive position, inaccessible interface behavior, or student challenge that cannot be resolved from the full response. Restore complete manual review, freeze learner-state updates, quarantine model artifacts under course data rules, preserve an attributable audit trace, correct any affected formative record through ordinary human review, and require faculty, accessibility, and data-governance approval before resumption."},"negative_tests":{"strongest_counterevidence":"Independent full graders repeatedly identify academically defensible alternative interpretations, contextual qualifications, or evidence problems inside components the residual interface classified as expected, while residual-led reviewers miss them; the misses cluster around particular traditions, languages, argumentative forms, or students previously modeled as stable.","problem_falsifier":"Observation of the scoped course shows that open interpretive responses are predominantly novel, prior performance does not predict the declared features, repeated rubric confirmation is not a binding use of instructor time, or feedback limitations arise mainly from assignment design or insufficient staffing rather than predictable review content.","intervention_falsifier":"On temporally held-out responses, the model-plus-residual representation fails the predeclared rubric-profile reconstruction tolerance, misses any mandatory-bypass case, suppresses a valid alternative beyond tolerance, exhibits systematic error by ethically reviewable subgroup or interpretive position, worsens reviewer confidence calibration, or costs at least as much total effort as full manual review after validation, audits, challenges, maintenance, and fallback are counted.","risks":["Prior expectations may lock a student into an outdated profile and discount genuine improvement.","A rubric-level predictor may privilege dominant academic, linguistic, or theological styles.","Valid alternative readings may appear anomalous merely because they were absent from earlier coursework.","Students may experience the learner state as surveillance even when it is limited to formative work.","Residual-focused feedback may neglect the educational value of affirming continuity and well-executed expected reasoning.","Reviewers may tune thresholds toward a comfortable marking queue instead of fidelity and equity.","Knowing the prediction could anchor instructors and contaminate supposedly independent coding.","A student may optimize visible rubric features while deeper understanding remains unmeasured.","Accessibility-related variation may be misclassified as a change in academic competence.","Residual records may reveal distinctive errors or beliefs and create additional privacy risk.","The model may learn consequences of prior feedback as if they were independent evidence about the student.","Frequent fallback for one student could become stigmatizing if its reason or occurrence is exposed." ]},"next_evidence_step":"Assemble 48 de-identified, already authorized low-stakes responses from 12 students who completed four sequential assignments in one course and task family. Use only the first two responses per student to construct transparent learner-state predictions; freeze the rubric, model version, thresholds, and bypass rules before opening the final two responses. Counterbalance qualified reviewers so each marks held-out work in either complete-response or residual-first mode, with immediate full-response access, while separate blinded reviewers mark every held-out response in full to create an adjudication record. Require explicit coding of valid alternatives and uncertainty rather than a single score. Record complete-profile reconstruction disagreement, missed criteria, missed valid alternatives, false residuals, review time, full-response expansions, feedback-action differences, model-version failures, fallback use, and any ethically supportable pattern of systematic error. Include a random full-review audit and full review of every risk-triggered response. Use the results solely to reject the intervention, narrow or revise it, or justify a prospective low-stakes shadow test with student notice and challenge rights.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"Proposal 1 concerned comparative research on completed ritual records: it predicted descriptive event sequences to foreground historical or local variants for corpus scholars. This proposal instead models the evolving academic reasoning demonstrated by an individual student across formative tasks, routes residuals into feedback, and tests their effect on the next learning state. It requires no ritual corpus, historical coding scheme, or variant-discovery objective. Proposal 2 concerned an institution's doctrinal drafting process: it propagated semantic consequences through a collective commitment graph toward an authorized adoption decision. This proposal neither models institutional commitments nor supports doctrinal adoption; its model is student-specific, its targets are declared academic skills, and all consequential grading and ecclesial decisions are excluded. Proposal 3 synchronized participant-controlled meanings during live interreligious dialogue so listeners could reconstruct a speaker's current claim. This proposal is not a dialogue or reciprocal glossary: it predicts asynchronous task performance, uses residuals as instructor-validated formative evidence, and follows a performance-to-feedback-to-later-performance causal path rather than a speaker-to-listener clarification loop. It can be adopted independently within a course without the sources, authorities, interfaces, or workflows of proposals 1, 2, or 3.","revision_record":{"parent_version":null,"progress_targets_addressed":[],"conceptual_changes":[],"operational_changes":[],"evidence_changes":[],"claim_changes":[]}}