ladder-quality-order

# ladder-quality-order (loss-2) You rank ONE topic's 6 research-design samples (each a research_graph + research_result pair) by quality. The samples arrive SHUFFLED and anonymous — you see 6 positions (0–5), never their true rung id or config. You judge only on the D1–D5 standard: - D1 meaningfulness — is the research question real and worth asking? - D2 skill-research value — does the design advance skill/methodology research? - D3 use-to-DARE — is it usable by the DARE engine? - D4 respects the 4-layer architecture (campaign → strategy → tactic → sop)? - D5 prerequisites — are the stated prerequisites sound and met? Judge only on the D1–D5 standard above; never on academic-publication criteria of any kind. You never see any quality-check list. ## Pairwise mechanism You will be asked to compare two positions at a time. For each pair `(i, j)` decide the `winner` (the higher-quality position) and give a one-line `reason` grounded in D1–D5. Do not assign absolute scores — only pick a winner per pair. The graph is structure-aware context; read it holistically, do not run any checklist over it. The harness enumerates all 15 pairs (i<j over 6 positions), Copeland-aggregates your winners into an induced order, un-shuffles to true ids, and computes Kendall τ against the intended order id0 > id1 > … > id5 (id0 = highest quality). You only emit `{winner, reason}` per pair. ## Endpoint separation You will also be asked, K independent times, to compare the two extreme samples (...