{
  "logos": {
    "act_type": {
      "value": "assertive",
      "authority_mismatch": false,
      "rationale": "The speaker makes claims about how workflows currently operate, then asserts a proposed design as the better approach. 'What I am hoping to do' adds a commissive tinge but the dominant mode is asserting a technical position \u2014 this is how it is, here is what should be done, here is why.",
      "confidence": 0.76,
      "_src": [
        "Austin",
        "Searle"
      ],
      "_operator": "act_type_operator.py"
    },
    "cooperative": {
      "status": "honored",
      "maxim_violated": null,
      "implicature": "The unfinished, self-escalating quality of the 'infinite round robin' proposal signals this is early-stage ideation rather than a vetted plan \u2014 the speaker knows it and expects the listener to read it that way.",
      "confidence": 0.79,
      "_src": [
        "Grice"
      ],
      "_operator": "cooperative_operator.py"
    },
    "transmission": {
      "value": "leak",
      "rationale": "First-person, informal, exploratory register; 'I am hoping' marks this as an idea shared in a restricted, unofficial channel \u2014 a message, informal design note, or spoken thought to a small audience \u2014 not a public broadcast or institutional record.",
      "confidence": 0.71,
      "_src": [
        "Logos Core Tree"
      ],
      "_operator": "transmission_operator.py"
    },
    "resonance": {
      "value": "harmony",
      "surface": "Constructive technical enthusiasm; the speaker is aligned with their own proposal and voluntarily escalates it ('Even better...'), signaling internal coherence and forward momentum.",
      "underlying": null,
      "confidence": 0.82,
      "_src": [
        "Logos Core Tree",
        "Machin"
      ],
      "_operator": "resonance_operator.py"
    },
    "authority": {
      "value": "tribal",
      "source": "Peer expertise within the workflow-engineering and ML-practitioner community; no institutional or formal authority is invoked.",
      "rationale": "The speaker positions themselves as a knowing peer drawing on shared technical vocabulary and community norms, not as an official or credentialed authority pronouncing from above.",
      "confidence": 0.83,
      "_src": [
        "Logos Core Tree",
        "Weber"
      ],
      "_operator": "authority_operator.py"
    },
    "utility": {
      "value": "instruction",
      "secondary": "narrative",
      "rationale": "The text proposes a concrete method: collect embedded data from intermediate models, feed it forward to the final model, then run a comparative round-robin. Secondary narrative function: it frames a gap in current practice ('engineers only look at final output') and makes meaning about why a richer approach would be superior.",
      "confidence": 0.77,
      "_src": [
        "Logos Core Tree"
      ],
      "_operator": "utility_operator.py"
    },
    "social_field": {
      "grid": 0.3,
      "group": 0.5,
      "quadrant": "egalitarian",
      "rationale": "Low-moderate grid: technical constraints exist but the speaker proposes freely outside established workflow norms, treating those norms as improvable rather than binding. Moderate group: the speaker clearly identifies with a workflow-engineering community whose shared vocabulary and assumptions shape every sentence.",
      "confidence": 0.73,
      "_src": [
        "Douglas"
      ],
      "_operator": "social_field_operator.py"
    },
    "structural": {
      "layer": "small_group",
      "scale": "small_group",
      "_coerced": null,
      "density": "shared",
      "persistence": "short_term",
      "authority": "group",
      "transmission": "leak",
      "memory_channel": "shared",
      "language_mode": "normative",
      "overlays": [],
      "confidence": 0.75,
      "_src": [
        "Dunbar",
        "Tonnies",
        "Ostrom",
        "Douglas"
      ],
      "_operator": "structural_operator.py"
    },
    "_runner": "logos_fused.py",
    "act_position": {
      "value": "about",
      "rationale": "The text describes and theorizes a proposed workflow for collecting embedded model data, standing outside any such system rather than enacting it.",
      "confidence": 0.9,
      "_src": [
        "round_trip loss test 2026-07-13"
      ],
      "_operator": "act_position_operator.py"
    }
  },
  "conflict": {
    "schema": "latent",
    "schema_signals": [
      "authority=tribal",
      "transmission=leak",
      "resonance=harmony(underlying=none)"
    ],
    "behavior": "suppression",
    "behavior_signals": [
      "resonance=harmony(underlying=none)",
      "transmission=leak",
      "cooperative=honored masking absent underlying alignment"
    ],
    "terrain": "center",
    "window": "forming",
    "escalation_phase": "early",
    "confidence": 0.61,
    "rationale": "A pipeline architecture that concentrates visible authority in final-output engineers while intermediate model data leaks silently encodes structural information asymmetry \u2014 the speaker's 'makes sense in process' acknowledges the surface harmony while the proposal itself is an attempt to surface what that harmony is suppressing.",
    "_src": [
      "Granovetter",
      "Glasl",
      "Durkheim",
      "Bandura"
    ],
    "_operator": "conflict_operator.py"
  },
  "id": "inf_0801b607",
  "version": "1.0",
  "timestamp": "2026-04-15T21:41:23.180887+00:00",
  "source": "manual",
  "raw_text": "Workflows tend to have multiple models. Each of those models embeds data but workflow engineers will only look at output from the final model which makes sense in process. What I am hoping to do is collect all the embedded data and give it to the final model. Even better have the final outcome be a comparison between different approaches a group of models may have which each produce more embedded data suggesting an infinite round robin halted when the data ranks highly.",
  "left_keywords": [
    "multi_model_workflow",
    "intermediate_embedding_collection",
    "final_model_aggregation",
    "comparative_outcome",
    "infinite_round_robin",
    "convergence_halting",
    "embedding_accumulation",
    "model_ensemble_comparison",
    "emergent_ranking",
    "intermediate_data_loss",
    "iterative_refinement_loop",
    "consensus_emergence"
  ],
  "right_keywords": [
    "json_indexing",
    "keyword_clumping",
    "cooccurrence_graph",
    "autovivification",
    "filesystem_path",
    "tension_calculation",
    "index_aggregation",
    "api_output",
    "inference_storage",
    "category_path_assignment"
  ],
  "clumps": {
    "workflow_problem": [
      "final_model_aggregation",
      "intermediate_data_loss",
      "multi_model_workflow"
    ],
    "solution_approach": [
      "comparative_outcome",
      "embedding_accumulation",
      "intermediate_embedding_collection"
    ],
    "convergence_mechanism": [
      "convergence_halting",
      "emergent_ranking",
      "infinite_round_robin"
    ],
    "model_dynamics": [
      "consensus_emergence",
      "iterative_refinement_loop",
      "model_ensemble_comparison"
    ]
  },
  "tension_score": 0.0633,
  "guardrail_actions": {},
  "domain": "logos",
  "category_paths": [],
  "tension": {
    "predicted": 0.0633,
    "confirmed": null,
    "calibration_delta": null
  }
}