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EkoH

EkoH (Reputation & Expertise) — first sub‑module under Kollective Intelligence.
Implements seven core services with clear code‑names, supported by dedicated models and fixed parameters.

1) Functional Services (and expected files)

Code‑name list per the v14 inventory; each code‑name maps to a Django service module (e.g., services/scoring.py contains multidimensional_scoring).

Display name Code name / service Purpose / behavior Likely file or module
Multidimensional Scoring multidimensional_scoring Compute per‑user/content scores across axes (quality, frequency, relevance, expertise). services/scoring.py
Criteria Customization configuration_weights Adjust scoring weights per axis/domain; read from stored configuration. services/configuration.py (reads ScoreConfiguration)
Automatic Contextual Analysis contextual_analysis AI tweaks sub‑scores in real time by topic/history/complexity signals. services/contextual_analysis.py
Dynamic Privacy privacy_settings Enforce anonymity/pseudonym modes while still exposing merit outputs. services/privacy.py
History & Traceability score_history Persist every recalculation/config change for auditability. services/history.py (+ model hooks)
Interactive Visualizations score_visualization Serve aggregated data for dashboards/skill maps/matrices. services/visualization.py
Expertise Classification by Field expertise_field_classification Bind scores to formal knowledge domains (taxonomy). services/expertise.py

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2) Backend Functionalities

  • Reputation engine & triggers. A Django service updates users’ domain‑specific Ekoh scores from platform activity; scheduled Celery jobs perform periodic recalculation, and event hooks apply immediate updates on impactful actions.

  • Ethical multiplier. An ethics score multiplies domain expertise to produce final influence weights (raises for constructive behavior, lowers for flagged behavior).

  • Smart‑Vote integration. Voting across modules (e.g., Ethikos) is weighted by the voter’s relevant Ekoh score; live results may be pushed via Channels.

  • Cross‑module APIs. Provides shared search/notifications/feed/recommendation surfaces that consume Ekoh signals (e.g., leaderboards, relevance).

  • Quality controls. Thresholds and moderation safeguards prevent brigading/spam from distorting reputation and consensus.

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3) Database Models (OLTP)

Canonical tables powering EkoH scoring, ethics, audit, and privacy.

Table / Model Purpose Key fields
ExpertiseCategory Domain taxonomy for expertise classification. id, name
UserExpertiseScore Per‑user per‑domain raw/weighted score. id, user, category, raw_score, weighted_score
UserEthicsScore Per‑user ethical multiplier (applied to expertise). user (PK), ethical_score
ScoreConfiguration Named weights/coefficients (global or per field). id, weight_name, weight_value, field
ContextAnalysisLog AI context adjustments applied to scores. id, entity_type, entity_id, field, input_metadata (JSON), adjustments_applied (JSON)
ConfidentialitySetting User privacy level for identity display near scores. user (PK), level (enum: public/pseudonym/anonymous)
ScoreHistory Full audit trail of score changes. id, merit_score (FK), old_value, new_value, change_reason

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4) Supporting Configuration (frozen)

Finalized parameters for EkoH engine and domain taxonomy.

  • Initial axis weights: quality=1.000, expertise=1.500, frequency=0.750 → used by multidimensional_scoring.

  • Ethical multiplier bounds: floor 0.20, cap 1.50.

  • Expertise domains: EXPERTISE_DOMAIN_CHOICES (26 ISO‑based domains; seeded fixtures).

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5) Schedules & runtime

  • Periodic recomputation: Celery Beat tasks (nightly/interval) to refresh Ekoh scores and any precomputed leaderboards; monitored in CI/ops.

  • Realtime delivery: Optionally push score/leaderboard deltas or weighted results via Django Channels + Redis.

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Summary

EkoH exposes seven concrete services (multidimensional_scoring, configuration_weights, contextual_analysis, privacy_settings, score_history, score_visualization, expertise_field_classification) mapped to Django service modules; it persists expertise/ethics/traceability/privacy via dedicated tables and operates under fixed, reviewable parameters. It is the weighting backbone for Smart‑Vote and cross‑module relevance, with periodic recomputation and optional realtime updates.