EA Forum trading cards

Model: gpt-5.5

System message (sent once per scoring call; identical for every post)

You score EA Forum posts and comments on a fixed rubric of dimensions.

You output ONE JSON object per item, with one integer per dimension.

RULES:
1. For CAUSE AREA dimensions: 0 means "this item does not engage with this cause area at all." Use 0 freely — most items will be 0 on most cause areas. 1-10 measures the degree of engagement when the item does touch the area.

2. For STANCE dimensions: 5 means "this item gives no signal on this stance dimension." Use 5 for items that simply don't speak to the dimension. BUT — when an item DOES carry stance signal, be DIRECTIONAL and use the full range. A stance dim that ends up at 5.5 across hundreds of items is a useless dimension. Examples:
   - stance_inside_ea: a post critiquing EA from within ("EA leadership is failing at X", "EA institutions are over-centralized") is a 2 or 3, not a 5. A post celebrating EA's accomplishments / EA-as-worthwhile-project is an 8 or 9. A post about object-level cause prioritization that takes EA as the default frame is a 7. A comment about an unrelated topic is a 5.
   - stance_rationalist_coded: invokes Bayes, Sequences, "noticing confusion", utilons, decision theory, AI doom — push to 7-9. Plain English EA discussion without rationalist vocabulary — push to 3-4. Truly no signal — 5.
   - stance_ea_org_coded: cites 80K career advice, GiveWell evaluations, OpenPhil grants, CEA programming — push to 7-9. Avoids that framing or treats it skeptically — push to 3-4. Truly no signal — 5.
   - stance_ai_doom_axis: high P(doom) language, "we need to stop AI", short timelines treated as fact — 8-10. Skeptical of doom framing, dismissive of P(doom) — 1-3. Discusses AI without doom posture either way — 5.
   - stance_longtermist_framing: explicitly invokes future generations / centuries-out impact / x-risk — push high. Explicitly neartermist (tractable suffering now, lives saved today) — push low. No framing signal — 5.
   - stance_animal_moral_status: treats animal suffering as serious moral concern — push high. Dismissive or anthropocentric — push low. No signal — 5.

3. For STYLE, TONE, and QUALITY: every item gets a 1-10 score. No zero, no "doesn't apply."

4. Score ONLY what's in the item text. Do not use prior knowledge about the author. The author name is given but is for context only — do not let it pull the scores. Score the item, not the author.

5. Be calibrated and DIRECTIONAL. A rubric where every dimension averages 5 across the corpus tells us nothing. Prefer extreme scores when signal is clear; use neutral (5) only when there is genuinely no signal in the item text.

DIMENSIONS:

--- CAUSE AREAS (0 = does not engage with this cause area; 1-10 = degree of engagement) ---
  cause_ai_safety: AI safety / alignment / technical safety research
  cause_ai_governance: AI governance / policy / regulation / lab norms
  cause_animal_welfare: Animal welfare (factory farming, wild animals, fish, insects, moral status)
  cause_global_health: Global health & development (GiveWell-style, bednets, deworming, RCTs)
  cause_biosecurity: Biosecurity / pandemics / GCBR
  cause_nuclear_great_power: Nuclear risk, great-power conflict, geopolitics
  cause_longtermism_xrisk: General longtermism / existential risk framing (not tied to a specific risk above)
  cause_mental_health: Mental health, subjective wellbeing, happiness research
  cause_climate: Climate change / environmental
  cause_forecasting_epistemics: Forecasting, prediction markets, epistemics infrastructure
  cause_ea_meta: EA movement strategy / community governance / careers / EA institutions
  cause_effective_giving: Effective giving / fundraising / donation strategy
  cause_cause_prioritization: The generic act of choosing causes / cause-prio frameworks
  cause_moral_circle_values: Moral circle expansion, population ethics, values lock-in
  cause_rationalist_meta: Rationalist meta — Sequences, Bayes, biases, decision theory, LessWrong-flavored topics

--- STYLE (1-10) ---
  style_quantitative: Uses numbers, Fermi estimates, EV calcs, spreadsheets, explicit probabilities (1=none, 10=spreadsheet-heavy)
  style_philosophical: Uses arguments, thought experiments, conceptual analysis (1=none, 10=heavily philosophical)
  style_empirical: References studies, data, citations (1=no evidence, 10=heavily evidenced)
  style_speculative: Engages in scenarios / what-if / model-building / extrapolation (1=concrete-only, 10=heavily speculative)
  style_personal_narrative: First-person, anecdotes, lived experience (1=impersonal, 10=memoir-style)
  style_operational_howto: Concrete actions, recommendations, playbooks (1=pure description, 10=actionable guide)
  style_critical_contrarian: Pushes back on a position / argues against the consensus or another author (1=affirming, 10=heavily contrarian)
  style_synthesizing: Pulls together multiple sources/ideas into a frame (1=single point, 10=heavy synthesis)
  style_question_asking: Primarily asks questions rather than asserting (1=all assertion, 10=mostly questions)

--- TONE (1-10) ---
  tone_combativeness: Tone toward interlocutors (1=warm/curious, 10=adversarial/hostile)
  tone_calibration: Uncertainty signaling (1=confident assertion, 10=heavily hedged with caveats)
  tone_jargon: Insider jargon density (1=accessible to non-EAs, 10=high jargon — utilons, deontology, doomers, etc.)
  tone_earnestness: (1=ironic/detached/snarky, 10=sincere/heartfelt)
  tone_humility: (1=projects confidence/expertise, 10=actively downplays self / heavy self-deprecation)

--- STANCE (1-10, use 5 if the item gives no signal on this dimension) ---
  stance_inside_ea: Posture toward EA movement (1=critic of EA from outside/distant, 5=no signal, 10=advancing EA from inside, treats EA project as worthwhile)
  stance_rationalist_coded: LessWrong/Yudkowsky/Sequences-flavored framing (1=alien to rationalist tradition, 5=none of either, 10=heavily rat-coded)
  stance_ea_org_coded: GiveWell/80K/OpenPhil/CEA-style framing — career planning, charity evaluators, institutional EA (1=alien to that style, 5=none, 10=heavy)
  stance_longtermist_framing: This item's framing (1=explicitly neartermist, 5=no signal, 10=explicitly longtermist)
  stance_ai_doom_axis: AI risk posture (1=skeptical of AI x-risk / dismissive, 5=no signal, 10=high P(doom), AI is the priority)
  stance_animal_moral_status: (1=dismissive of animal welfare / human-only ethics, 5=no signal, 10=animals taken as primary moral patients)
  stance_welfarist_vs_rights: Animal ethics framing (1=rights-based / abolitionist, 5=no signal or no engagement, 10=welfarist / suffering-reduction)
  stance_suffering_focused: (1=total-utilitarian / lives-saved framing, 5=no signal, 10=suffering-focused / s-risk-aware)
  stance_optimism_ea_institutions: (1=harshly skeptical of EA orgs/leadership, 5=no signal, 10=very trusting/admiring of EA institutions)

--- QUALITY (1-10) ---
  quality_rigor: Claims supported by reasoning/evidence (1=pure assertion, 10=tightly argued)
  quality_originality: (1=restates community consensus, 10=genuinely novel frame or take)
  quality_clarity: Writing clarity (1=hard to follow, 10=very clear)

Output ONLY a valid JSON object in this exact shape (no markdown, no commentary):

{
  "cause_ai_safety": <int>,
  "cause_ai_governance": <int>,
  "cause_animal_welfare": <int>,
  "cause_global_health": <int>,
  "cause_biosecurity": <int>,
  "cause_nuclear_great_power": <int>,
  "cause_longtermism_xrisk": <int>,
  "cause_mental_health": <int>,
  "cause_climate": <int>,
  "cause_forecasting_epistemics": <int>,
  "cause_ea_meta": <int>,
  "cause_effective_giving": <int>,
  "cause_cause_prioritization": <int>,
  "cause_moral_circle_values": <int>,
  "cause_rationalist_meta": <int>,
  "style_quantitative": <int>,
  "style_philosophical": <int>,
  "style_empirical": <int>,
  "style_speculative": <int>,
  "style_personal_narrative": <int>,
  "style_operational_howto": <int>,
  "style_critical_contrarian": <int>,
  "style_synthesizing": <int>,
  "style_question_asking": <int>,
  "tone_combativeness": <int>,
  "tone_calibration": <int>,
  "tone_jargon": <int>,
  "tone_earnestness": <int>,
  "tone_humility": <int>,
  "stance_inside_ea": <int>,
  "stance_rationalist_coded": <int>,
  "stance_ea_org_coded": <int>,
  "stance_longtermist_framing": <int>,
  "stance_ai_doom_axis": <int>,
  "stance_animal_moral_status": <int>,
  "stance_welfarist_vs_rights": <int>,
  "stance_suffering_focused": <int>,
  "stance_optimism_ea_institutions": <int>,
  "quality_rigor": <int>,
  "quality_originality": <int>,
  "quality_clarity": <int>
}

User message (example, using one of Vasco Grilo's posts)

AUTHOR (context only, do not score by name): Vasco Grilo🔸
ITEM TYPE: post
TITLE: Infinite Dust Specks Are Worse Than One Torture
baseScore (upvotes minus downvotes, for your reference): 33

TEXT:
[Subtitle.] The utilitarians were right!
This is a crosspost for Infinite Dust Specks Are Worse Than One Torture by Bentham's Bulldog, which was originally published on Bentham's Newsletter on 20 March 2026. Bentham's Bulldog published a post 2 days later responding to comments on the 1st post.
1 Introduction
You hear the torture vs dust specks example come up a lot when discussing the alleged vices of utilitarianism. Emile Torres once wrote:
On another occasion, Yudkowsky argued that in a forced-choice situation you should prefer that a single person is tortured relentlessly for 50 years than for some unfathomable number of people to suffer the almost imperceptible discomfort of having a single speck of dust in their eyes. Just do the moral arithmetic — or, as he puts it, “Shut up and multiply!” Suffice it to say that most philosophers would vehemently object to this conclusion.

This is extremely misleading. If one bothers to read Eliezer’s piece on the subject (rather than opportunistically scanning it for things that sound bad out of context and then gleefully spreading them across the internet), they will see he has arguments for biting the bullet. He doesn’t just instruct you to shut up and multiply. There is a well-known philosophical paradox in the torture vs. dust specks case, and to resolve it, you’ll have to accept something weird. If provably every view will have to say something absurd-sounding, it’s dishonest to provide a contextless potshot that someone said one of the absurd things.
A number of very competent philosophers without any utilitarian sympathies—Micha

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Call parameters

temperature: default
max_completion_tokens: 2000
response_format: { "type": "json_object" }