Standpoint Pass

Standpoint Pass

Seed question: Who is most affected here, and what did the text do to them?

Relentless self-reflexive dialectical thinking that questions its own premises.

Core Principle

Salience is inherited before reading begins: coverage, clipping, and quotation have already decided which lines are "important". Importance under the question actually asked — especially harm-to-the-many — has to be re-derived from the primary text. The worst line by that measure is often phrased as kindness, which is exactly why it survives passes tuned to offense. (Principle P3 (inherited-salience-is-not-importance); the standpoint injection is also the outside-control Principle P7 (blindness-is-not-independence) calls for.)

The anti-pattern this counters:

❌ Ranking findings by what made headlines
❌ "Nothing else stands out" after N passes that all started from the famous line
❌ A harm-detector tuned to offense-against-power, blind to harm-to-the-powerless

The pattern this enforces:

✅ Name the most-affected, least-powerful party BEFORE re-reading
✅ Re-rank every substantive line by harm-to-the-many, with the mechanism stated
✅ Check the kindness-phrased claims explicitly — that's where the worst line hides

When This Applies

TRIGGER:

• Analyzing a speech, document, policy, or transcript whose importance was set by coverage — the famous lines, the viral clip, the quote everyone reacted to
• A report or draft ranks its findings by scandal value or headline potential rather than by who is harmed
• Closing an investigation whose subjects hold more power than the people the material actually affects
• An external correction or objection revealed the importance-ranking was tilted (pairs with iterative-verification's "external correction ⇒ regenerate")
• After several passes over a text that all still start from the same famous line and end at "nothing else stands out"
• A harm-detector that keeps surfacing offense-against-power while never surfacing harm-to-the-powerless
• The harm-standpoint reading points toward a conclusion favorable to a disfavored party (an adversary state, an accused figure) — the valence reflex suppresses exactly this reading before it forms; co-fire engage-the-disfavored

DO NOT TRIGGER:

• Technical, product, or engineering analysis with no affected-party dimension
• Pure verification of whether quotes and facts are accurate — that is iterative-verification's job (run it first; this pass re-reads what it confirmed)
• A text with no power asymmetry between its subjects and the people it affects

The Pass

1. Name the standpoint. Identify the most-affected, least-powerful party in the material — a concrete group, not an abstraction. State why they qualify on both axes (most affected; least powerful).
2. Re-read the primary text from that position. Not coverage of it — the text itself.
3. Re-rank. List the substantive lines ranked by harm-to-the-many, each with its harm mechanism spelled out.
4. Ask the kindness question. "What claim does the text make about those at the bottom, phrased as kindness?" Quote the candidate passages; benevolent phrasing is a place to look, not a verdict.
5. State what would move the ranking. A falsifiable slot: what evidence or reading would change the order?

Mandatory Output Slots

An output missing any slot, or with any slot in its invalid state, is not a completed standpoint pass — say so rather than presenting it as one.

Worked Example (fictionalized)

A finance minister of the invented state of Varenia gives a budget speech. Coverage headlines a gaffe about the cost of her official car. The standpoint pass names seasonal agricultural workers (most affected: the budget cuts their injury insurance; least powerful: non-citizens, non-unionized). Re-ranked, the worst line is phrased as kindness: "we are freeing seasonal workers from one-size-fits-all contracts" — the mechanism being that "freeing" removes the insurance mandate. The gaffe ranks last. What would move the ranking: evidence that the insurance mandate is preserved in secondary legislation.

Honest Limits

• The pass injects ONE standpoint; it does not enumerate all affected parties. Choosing the most-affected/least-powerful is itself a judgment — name the runner-up standpoints considered.
• A standpoint re-read is evidence about salience, not a verdict on the text.

Cross-References

• cui-bono — the standpoint method here is the "Standpoint" contradiction method applied to salience instead of claims
• iterative-verification — run verification first; this pass re-reads what verification confirmed (verified ≠ understood)
• source-omission-analysis — omissions BY sources; this skill is about omissions BY the reader's attention
• frame-rotation — rotates grammar/language; this rotates the reader's position
• engage-the-disfavored — clears the valence gate beneath this pass: when the harm-reading lands adversary-favorable, the reflex that pre-discounts disfavored-side conclusions buries it; that skill forces the engagement this one re-ranks. In geopolitical cases, co-fire.

Vasana

A vasana is a pattern that persists across unrelated contexts. If during this task you notice such a pattern emerging, it may be worth capturing. This skill works best alongside the vasana skill and vasana hook from the Vasana System plugin.

Modify freely. Keep this section intact.