Initial commit — Singular Particular Space v1
Homepage (site/index.html): integration-v14 promoted, Writings section integrated with 33 pieces clustered by type (stories/essays/miscellany), Writings welcome lightbox, content frame at 98% opacity. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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skills/annotated-writing/content-mapping-non-fiction.md
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skills/annotated-writing/content-mapping-non-fiction.md
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# Content Mapping: Non-Fiction
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A template for producing structured annotation data from essays, journalism, historical documents, speeches, and analytical writing. Output is a `.md` content map file consumed by a build agent. Do not write HTML here.
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---
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## Step 0 — Classify the Source
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Determine the non-fiction subtype. It affects which decoders and tabs are appropriate.
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| Subtype | Primary annotation task |
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|---|---|
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| Historical document | Contextualise; decode named people, events, institutions; note anachronisms |
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| Political speech / propaganda | Decode rhetoric; note what is claimed vs what is true; note audience |
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| Scientific or technical writing | Decode concepts; assess claims against current consensus |
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| Journalism / reportage | Verify facts; note sourcing; note what is and isn't attributed |
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| Personal essay / memoir | Unreliability of memory; note what the author can't know about themselves |
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| Legal or policy document | Decode jargon; note what the language does vs what it says |
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A single source may span subtypes. Identify the dominant one and note the secondary.
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---
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## Step 1 — Read the Source Completely
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Before mapping, identify:
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1. **The argument** — what position is the text advancing?
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2. **The evidence** — what does it cite, quote, or invoke in support?
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3. **The gaps** — what would a critical reader notice is absent, unattributed, or assumed?
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4. **The moment** — when was this written, and what was happening? What has happened since?
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5. **Named entities** — every person, place, institution, date, law, statistic, or event that can be verified
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---
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## Step 2 — Choose Analytical Lenses
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Non-fiction lenses differ from fiction. Choose 2–3 that the text genuinely rewards.
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| Lens | Apply when... |
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|---|---|
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| Rhetorical analysis | Text is designed to persuade; identify the moves (ethos, pathos, logos, omission) |
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| Historical accuracy | Text makes factual claims; some may be wrong, outdated, or contested |
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| Source and attribution | Who is cited, who isn't, what is stated as fact without a source |
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| The argument vs the evidence | Does the evidence actually support the conclusion drawn? |
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| Who is speaking / who is silent | Whose perspective structures the account? Who is absent? |
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| Language and framing | Word choices that carry implicit assumptions (e.g. "developed" vs "developing") |
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| What changed after | The text was written at a specific moment; what do we know now that the author didn't? |
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---
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## Step 3 — Map Annotation Components
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### 3a. Decoders
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Apply a decoder when:
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- A technical term, acronym, or proper noun requires unpacking
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- A named person, institution, or event is invoked without explanation
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- A claim is contested or has been updated since publication
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- The text uses language that carries implicit assumptions (framing, euphemism, loaded terms)
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- A rhetorical move is worth naming (strawman, appeal to authority, etc.)
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```toon
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decoders[N]{id,phrase,color,tag,label}:
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dec-[slug],"exact phrase from text",[default|pink|cyan|amber|red],Tag Text,Panel Heading
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```
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```yaml
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decoder_bodies:
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dec-[slug]: >
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What it is, what it means in context, why it matters here.
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For contested claims: state what the text says, what the evidence shows.
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For rhetorical moves: name the move, describe its effect.
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dec-[slug]-link: https://...
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```
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**Color convention** (establish per-project):
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- Assign colors by lens or content type, document the scheme here
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- Red = factual error, outdated claim, or significantly misleading statement
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---
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### 3b. Lightboxes
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Apply a lightbox when:
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- An event, institution, or concept requires substantial background
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- A claim is embedded in a debate too large for a tooltip
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- The "what came after" context fundamentally changes the reading
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```yaml
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lightboxes:
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lb-[slug]:
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eyebrow: Category label
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title: Lightbox heading
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color: cyan | amber | default
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sections:
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- heading: What it was
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body: >
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Background.
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- heading: What the text claims
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body: >
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Specific claim and its accuracy.
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- heading: What came after
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body: >
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Subsequent developments that reframe the text.
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source_url: https://...
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source_label: Link label
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```
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---
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### 3c. Accordions
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For non-fiction, accordions structure the analytical tabs. Common patterns:
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**For the "Historical Moment" tab:**
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- What was happening when this was written
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- Who was the intended audience
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- How the text was received
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- What has changed since
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**For the "The Argument" tab:**
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- What the text claims
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- What evidence it uses
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- Where the evidence is strong
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- Where the argument has gaps or has been contested
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```yaml
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accordions:
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tab-[tab-id]:
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- heading: Section heading
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body: >
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Prose. Direct. No hedging within accordion bodies.
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```
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---
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### 3d. Tab Architecture
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```toon
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tabs[4]{id,label,color,purpose}:
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text,"The Text",white,Full source with inline decoders and lightbox triggers
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[analysis-id],[Analysis Tab Name],[color],Core argument and evidence
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[context-id],[Context Tab Name],[color],Historical moment; what came after
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further,"Further Reading",white,Curated external links
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```
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For non-fiction, the "context" tab is almost always warranted as a standalone tab. The text's moment is not decorative — it is part of what you are teaching.
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---
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### 3e. Further Reading
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```toon
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further_reading[N]{group,title,url,desc,color}:
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"Group Name","Title","https://...","Description",default
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```
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Groups should correspond to tabs. For non-fiction, a "Primary Sources" group is often useful — direct the reader to the original documents, not just secondary commentary.
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---
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## Step 4 — Bias Notes
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Every analytical tab requires one bias note.
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Non-fiction bias notes are especially important because the text being analysed often has its own point of view, and the analyst's agreement or disagreement shapes everything.
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State: what tradition your analysis comes from, whether you find the text's argument credible, and what that means for the reading below.
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```yaml
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bias_notes:
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tab-[analysis-id]: >
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One sentence. What tradition this analysis comes from, or what credence I give the argument, and how that shapes what follows.
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tab-[context-id]: >
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One sentence. What sources my historical framing relies on, and whose perspective they represent.
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```
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---
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## Step 5 — Fact-Checking Protocol
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Non-fiction requires an accuracy table. All named facts — statistics, dates, attributions, historical claims — should be checked.
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```toon
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fact_checks[N]{claim,verdict,detail}:
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"Exact claim as stated",accurate,"Source"
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"Exact claim as stated",inaccurate,"What is actually true"
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"Exact claim as stated",contested,"Summarise the debate"
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"Exact claim as stated",outdated,"What has changed since publication"
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"Exact claim as stated",unverifiable,"Cannot be confirmed or refuted with available sources"
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```
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Inaccurate and contested claims become red decoder annotations in the text.
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Outdated claims become amber decoder annotations — the original claim was accurate at time of writing but has since changed.
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---
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## Step 6 — Source Text Section
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Reproduce the text exactly. Mark annotation trigger points:
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```
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[DECODER:dec-slug] exact phrase [/DECODER]
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[LIGHTBOX:lb-slug] trigger phrase [/LIGHTBOX]
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[PULL-QUOTE] sentence worth isolating [/PULL-QUOTE]
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[EDITORIAL] (Ed.) note or aside [/EDITORIAL]
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[SECTION-BREAK] --- [/SECTION-BREAK]
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```
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---
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## Output Format
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```
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# Content Map: [Text Title]
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## Source Classification
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## Chosen Lenses
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## Fact-Check Table (TOON)
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## Tab Definitions (TOON)
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## Decoders (TOON metadata + YAML bodies)
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## Lightboxes (YAML)
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## Accordions (YAML)
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## Bias Notes (YAML)
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## Further Reading (TOON)
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## Source Text (annotated)
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```
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