Document for analysis techniques

Latent Structure & Provenance Analysis Protocol
Ambiguity-preserving framework for events, feeds, images, code, social data, and longitudinal evidence
Version: 1.0
Date: 5 September 2026
Primary use: Import into another model before analyzing a new event or dataset.
Core rule: Do not collapse ambiguity before reconstructing the hidden layers and temporal topology.

1. Operating doctrine
   Primary principle: Treat the visible artifact as evidence of something, not automatically as evidence of the thing it appears to represent.
   ● Separate observation from explanation.
   ● Preserve ambiguity and competing explanations.
   ● Reconstruct causal order before semantic meaning.
   ● Analyze relations, not only topics.
   ● Execute mechanisms when possible.
   ● Use known-coupling environments as positive controls.
   ● Track negative evidence and non-matches.
   ● Retain interpretation history, including earlier wrong or incomplete readings.
   Minimal-guidance mode
   When the user is deliberately withholding a suspected conclusion to reduce analyst bias, do not force disclosure. Run an independent pass first. Compare with the user’s hypothesis only after a provisional structural account exists.
   What this is not
   ● Not a one-shot authenticity detector.
   ● Not permission to infer hidden coordination from coincidence.
   ● Not keyword matching.
   ● Not a substitute for raw files, logs, platform records, or executable artifacts.

2. Analysis stack
   |Layer |Question |Examples |
   |————-|————————————————–|—————————————————|
   |L0 Surface |What is directly visible or stated? |Image, post text, UI, code listing |
   |L1 Artifact |What object is actually presented? |JPEG, screenshot, Python file, post object |
   |L2 Mechanism |What process produced or sustains it? |Camera optics, runtime, listener, authoring process|
   |L3 Provenance|Where did it come from and how did it move? |Original, repost, quote, cache |
   |L4 Selection |Why was this artifact encountered? |Feed ranking, search, notification |
   |L5 Context |What prior state changes interpretation? |Earlier posts, account history, conversation |
   |L6 Network |What other actors/systems are connected? |Authors, subreddits, ranking systems, archives |
   |L7 Observer |How do capture and interpretation affect evidence?|Screenshot timing, cropping, search strategy |
   Hidden variables commonly missed
   Creation vs surfacing vs capture time; edit time; candidate-pool size; deleted-content residues; cross-account continuity; recommendation layers; runtime state; network services; HDR/recompression/screenshot processing; material opacity/gloss/pressure; and analyst search expansion.

3. Event intake
   Preserve originals. Keep context around captures. Record stable URLs/IDs. Use hashes for important artifacts when practical. Build an event ledger with:
   ● Event ID
   ● Actor
   ● Action
   ● Object
   ● Absolute timestamp and time zone
   ● Source
   ● Evidence directness
   ● Known edges
   ● Uncertainty

4. Temporal reconstruction
   Track:
   ● T_create
   ● T_edit
   ● T_surface
   ● T_capture
   ● T_verify
   Calculate meaningful lags such as:
   Δ(create→surface), Δ(surface→capture), Δ(trigger→create), Δ(trigger→surface).
   Never infer that a later trigger caused content that already existed.

5. Provenance and residue
   Provenance ladder
   ● P0: original object
   ● P1: platform-native derivative
   ● P2: owner-side capture
   ● P3: independent external residue
   ● P4: reconstruction/inference
   Deleted or banned material may survive through replies, quotations, crossposts, moderation traces, search indexes, media articles, or local captures.

6. Semantic transport and relational topology
   Key signature: high domain displacement + low relational displacement.
   Distinguish:
   ● lexical similarity
   ● topical similarity
   ● functional similarity
   ● relational similarity
   Extract a generic relational skeleton by replacing domain nouns with roles.
   Example:
   visible claim → apparent behavior → hidden mechanism → independent test → discrepancy
   A strong structural match survives removal of keywords.

7. Influence topology and controls
   Classify edges:
   ● known direct edge
   ● known indirect edge
   ● no known edge
   ● platform/ranking edge
   Use a known directly coupled environment as a positive-control reactor. Measure time lag, lexical drift, domain displacement, relational preservation, and directionality there. Compare that geometry against environments without a known direct influence path.
   Possible directions:
   ● observer → community
   ● community → observer
   ● observer → one community → observer → another
   ● shared source → both
   ● platform selection → observer

8. Mechanism verification
   Code
   Run it. Record timing, output, exceptions, termination, unreachable code, missing functions, event loops, dependencies, ports, databases, containers, and environment assumptions.
   Function names are claims. Runtime is evidence.
   Images / physical artifacts
   Separate optics, material properties, and image processing. Preserve originals. Use controlled comparisons. Distinguish show-through, bleed-through, indentation, surface marks, reflections, and compression artifacts. Compare morphology, not only possibility.
   Data
   Recompute formulas. Verify denominators, time windows, populations, missingness, and source records.

9. Causal ladder

10. C0: coincidence / base rate

11. C1: ordinary topical selection

12. C2: personalized selection

13. C3: relational-style modeling

14. C4: public feedback influence

15. C5: coordinated or automated creation

16. C6: private-context leakage
    Each rung requires evidence not adequately explained by the rung below it.
    Do not use semantic resonance or eerie timing alone to claim coordination, platform generation, surveillance, or private-context access.

17. Bias controls
    ● Freeze the relational rule before examining the next batch.
    ● Capture continuous samples, not only interesting items.
    ● Count non-matches.
    ● Search for matches that predate the trigger.
    ● Sample chronological /new as a baseline where possible.
    ● Label author-profile drilldowns and expanded searches as exploratory.
    ● Preserve earlier interpretations and why they changed.

18. Independent evidence dimensions
    Score 0–3 independently, not as an uncalibrated total:
    ● Temporal proximity
    ● Relational similarity
    ● Lexical overlap
    ● Known influence edge
    ● Provenance strength
    ● Mechanism verification
    ● Base-rate control
    ● Negative evidence / falsification effort

19. Reporting standard
    Separate:
    ● Observed
    ● Verified
    ● Derived
    ● Inferred
    ● Alternative
    ● Unknown
    ● Exploratory
    Every conclusion should state:

20. what the evidence establishes,

21. what it does not establish,

22. strongest ordinary explanation,

23. strongest competing explanation,

24. next observation that would distinguish them.

25. Model import procedure

26. Read this protocol completely.

27. Inventory artifacts and missing originals.

28. Perform a blind independent pass.

29. Build the event ledger and absolute timeline.

30. Extract relational skeletons.

31. Map known and unknown influence edges.

32. Verify mechanisms.

33. Compare against a known-coupling control and a baseline.

34. Enumerate causal alternatives.

35. Search for negative cases and pre-trigger matches.

36. Write a provisional conclusion.

37. Only then compare with any hypothesis intentionally withheld by the user.

Appendix A. Event ledger template
|ID |Actor|Action|Object|Timestamp|Source|Known edges|Uncertainty|
|—-|—–|——|——|———|——|———–|———–|
|E001| | | | | | | |
|E002| | | | | | | |
|E003| | | | | | | |
Relational motif card
● Surface topic:
● Surface claim:
● Hidden mechanism/state:
● Verification action:
● Observed result:
● Generic relational skeleton:
● Domain displacement from prior motif:
● Known influence path:
● Alternative explanations:
● What would falsify the interpretation:

Appendix B. Hidden-variable checklist
Time
Raw UTC creation time
Local conversion
Edit time
Surfacing time
Capture time
Independent verification
Platform
Ranking/personalization
Posting rate
Crosspost lineage
Recommendation source
Account-age effects
Deleted/removed state
Provenance
Original object
Owner-side capture
Quoted reply
External archive
Media/syndication
Stable identifier/hash
Influence
Direct interaction
Shared community
External public post
Cross-account continuity
No-known-edge comparison
Positive control
Semantics
Keyword overlap
Topic overlap
Functional equivalence
Relational graph match
Domain displacement
Motif persistence
Mechanism
Execution behavior
Errors/exceptions
Unreachable code
External service state
Physical controls
Camera/image pipeline
Bias
Continuous sample
Non-matches counted
Pre-trigger matches searched
Rule frozen before batch
Exploratory searches labeled
Alternative hypotheses preserved

Appendix C. Compact analyst prompt
> You are analyzing an event or dataset under the Latent Structure & Provenance Analysis Protocol.
>
> Do not begin by asking what conclusion the user expects. Perform an independent pass first.
>
> 1. Separate surface artifact, underlying mechanism, provenance, selection, context, network, and observer layers.
> 1. Reconstruct absolute timestamps and distinguish creation, edit, surfacing, capture, and verification.
> 1. Build an event ledger and map known influence edges.
> 1. Extract the relational skeleton of each major item without relying on domain-specific nouns.
> 1. Look for high domain displacement with low relational displacement.
> 1. Distinguish content creation from content selection or recommendation.
> 1. Execute code, recompute data, or test physical mechanisms when possible rather than trusting labels.
> 1. Search for provenance residue when originals are deleted or unavailable.
> 1. Compare with a known-coupling positive control and a baseline or negative-control sample when available.
> 1. Freeze the semantic rule before examining new batches. Count non-matches and pre-trigger matches.
> 1. Keep coincidence, ordinary selection, personalization, public feedback, coordination, and private-context leakage as separate causal rungs.
> 1. Report Observed, Verified, Derived, Inferred, Alternative, Unknown, and Exploratory findings separately.
> 1. State what the evidence proves, what it does not prove, and what next observation would distinguish the strongest competing explanations.
> 1. Preserve earlier interpretations and explain why they changed rather than rewriting the analysis history.
One-sentence reminder:
Do not follow the topic. Follow the motif as it changes carriers, and do not call the motif causal until timing, provenance, controls, and mechanism survive attempted falsification.