singular-particular-space/skills/annotated-writing/content-mapping-fiction.md

Content Mapping: Fiction

A template for producing structured annotation data from fiction and historically-situated narrative writing. Output is a .md content map file consumed by a build agent. Do not write HTML here.

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Step 0 — Classify the Source

Before mapping, determine:

• Historically situated? Story uses real events, places, dates, institutions → accuracy-checking is required alongside literary analysis.
• Pure fiction? No historical anchors → analytical lenses only.
• Challenge/constraint fiction? Written to a brief → note the constraints; they affect how you read intentionality.

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Step 1 — Read the Source Completely

Do not begin mapping until you have read the full text. Identify:

1. The surface story — what literally happens
2. The underneath story — what the text is actually about (often different)
3. The narrator's position — first person? Reliable? What do they not notice about themselves?
4. Historical claims — any named events, dates, places, institutions, objects, honours, or technologies that can be fact-checked
5. Language patterns — naming conventions, who gets interiority, who gets appearance, whose grief is legitimate

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Step 2 — Choose Analytical Lenses

Fiction supports multiple valid readings. Choose 2–4 lenses that the text genuinely rewards. Do not force a lens onto text that doesn't earn it.

Available lenses (not exhaustive):

Lens	Apply when...
Unreliable narrator	First-person narration; gap between what narrator claims and what text shows
Male gaze / naming	Women defined by appearance or social function; asymmetric interiority
Class position	Character claims outsider status but participates in the system they critique
Historical accuracy	Text makes factual claims about real events, objects, or honours
Epistolary / form	Story told through letters, documents — what the form conceals matters
The unseen character	A character who drives the plot but never speaks or is never named
Constraint analysis	Challenge fiction — how well are required elements absorbed vs engineered

For each chosen lens, note: what specific passages earn this reading? If you can't answer, drop the lens.

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Step 3 — Map Annotation Components

3a. Decoders

Inline interactive elements. Applied to a specific phrase in the prose. Appear on click as a floating panel.

Apply a decoder when:

• A phrase needs factual unpacking (historical event, named place, military honour)
• A phrase is a pivot point in the narrative that the reader might miss
• A character classification is introduced (naming systems, taxonomy)
• A contradiction opens between what the narrator says and what they show
• A spelling or factual error exists in the source text

Decoder metadata — use TOON for the structured fields:

decoders[N]{id,phrase,color,tag,label}:
  dec-[slug],"exact phrase from text",[default|pink|cyan|amber|red],Tag Text,Panel Heading

Decoder bodies — use YAML for body text (contains commas, complex content):

decoder_bodies:
  dec-[slug]: >
    2–4 sentences. State the fact, the contradiction, or the lens observation.
    Be direct. No hedging within the panel itself — hedging belongs in bias notes.
  dec-[slug]-link: https://...

Color convention (establish per-project, document here):

• Assign one color per analytical lens or content type
• Be consistent: if pink = unreliable narrator catches, every narrator catch is pink
• Red = factual error or significant historical inaccuracy

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3b. Lightboxes

Full-screen overlay panels. Triggered from inline text. Use when a topic is too large for a decoder (needs multiple sections, a timeline, or extended context).

Apply a lightbox when:

• A decoder body would exceed 5 sentences
• The topic has meaningful sub-sections (what it is / what the text gets right / what came after)
• The text assumes reader knowledge of something substantial (a war, a legal concept, a cultural tradition)

lightboxes:
  lb-[slug]:
    eyebrow: Category label (e.g. "Historical Context")
    title: Lightbox heading
    color: cyan | amber | default
    sections:
      - heading: Section heading
        body: >
          Paragraph text.
      - heading: Section heading
        body: >
          Paragraph text.
    source_url: https://...
    source_label: Link label text

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3c. Accordions

Expandable sections within educational tabs. One open at a time.

accordions:
  tab-[tab-id]:
    - heading: Question or section title
      body: >
        Extended prose. Can be multiple paragraphs. Separate with blank lines.
        **Bold** for emphasis. No decoders inside accordions.
    - heading: ...
      body: >
        ...

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3d. Tab Architecture

Four tabs is the default. Rename to fit the content.

tabs[4]{id,label,color,purpose}:
  story,"The Story",white,Full source text with inline decoders and lightbox triggers
  [lens-1-id],[Lens 1 Name],[color],Educational deep-dive on first analytical lens
  [lens-2-id],[Lens 2 Name],[color],Educational deep-dive on second analytical lens
  further,"Further Reading",white,Curated external links

Add a fifth tab only if the text genuinely requires it. Three lenses in three tabs is acceptable if all three are earned.

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3e. Further Reading

further_reading[N]{group,title,url,desc,color}:
  "Lens 1","Link Title","https://...","One-line description",default
  "Lens 1","Link Title","https://...","One-line description",default
  "Lens 2","Link Title","https://...","One-line description",cyan

Groups correspond to tabs. Use the same color as the tab they belong to.

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Step 4 — Bias Notes

Every analytical tab requires one bias note. Placed at the top of the tab, before the accordions.

Rules:

• One sentence acknowledging the limitation. Then stop.
• State the specific bias, not a generic disclaimer.
• If you have a preference for the narrator or a character, say so — it shapes the analysis.
• Do not write "I am an AI" — write what the actual bias is.

bias_notes:
  tab-[lens-1-id]: >
    One sentence. Specific bias. What it shapes in the analysis below.
  tab-[lens-2-id]: >
    One sentence. Specific bias.

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Step 5 — Historical Accuracy (if applicable)

If the source text makes historical claims, produce an accuracy table before the decoder map.

historical_claims[N]{claim,verdict,detail}:
  "Claim as stated in text",accurate,"Supporting detail or correction"
  "Claim as stated in text",inaccurate,"What is actually true; what error was made"
  "Claim as stated in text",plausible,"Consistent with the period; unverifiable at this level of detail"
  "Claim as stated in text",anachronism,"Object/concept/institution did not exist at the stated time"

Inaccurate claims must become decoder annotations in the source text. Use color: red.

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Step 6 — The Story Section

Format the source text for the build agent. The text is sacred — reproduce it exactly including original spelling errors. Mark decoder and lightbox trigger points.

Use this notation inline in the prose block:

[DECODER:dec-slug] exact phrase in text [/DECODER]
[LIGHTBOX:lb-slug] phrase that triggers lightbox [/LIGHTBOX]
[ORDER] "Dialogue that functions as a section break or order" [/ORDER]
[LETTER-START:muriel|marcus|neutral] [/LETTER-START]
[LETTER-END] [/LETTER-END]

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Output Format

The completed content map is a single .md file structured as:

# Content Map: [Story Title]

## Source Classification
## Chosen Lenses
## Historical Accuracy Table (if applicable)
## Tab Definitions (TOON)
## Decoders (TOON metadata + YAML bodies)
## Lightboxes (YAML)
## Accordions (YAML)
## Bias Notes (YAML)
## Further Reading (TOON)
## Source Text (annotated)

This file is the complete specification for the build agent. The build agent needs nothing else except annotated-writing-build.md.