| paper | d4-conf-aaai-2011-0027 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The paper contains no numbered theorem, lemma, corollary, or proposition asserting a computational result; it evaluates the tournament procedure through experiments. Since bit (a) fails, there is no anchored continuous question to grade, and the worker and answer structures proposed by the opponent would create a new computational problem.
fails bit a — no named computational result to mirror
The paper reports experimental outcomes for crowd responses and tournament selection, but has no named computational result for a continuous mirror to cover.
This paper contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational complexity result. Its results are empirical: the experiments report that tournament selection made one Chinese idiom’s correct translation reach about \(90\%\) after five rounds.
That observation is not an eligible anchor under the requested criteria. I therefore can’t give a continuous problem anchored in a named result, or classify its complexity, without inventing a result the paper does not claim.
The proponent offers no anchor, so there is no named result to survive by silence. The paper reports experimental outcomes: in particular, one Chinese idiom’s correct translation reached about \(90\%\) after five rounds. It gives no theorem, complexity classification, or algorithmic guarantee for a formally specified computational problem. The procedure itself is an algorithm, but describing steps is not a result about its complexity.
The strongest possible mirror would model a distribution of workers and answers, with worker types specifying how they produce answers and judge pairs. One could then ask whether a correct answer gains mass through repeated tournament rounds, or how to allocate comparisons. That might be a worthwhile new model, but this paper does not supply its essential inputs: a finite answer space, a type distribution, or a formal rule connecting worker types to comparison outcomes. In the experiments, answers are open-ended strings, correctness is assessed manually, and the central claim about comparisons is empirical. Supplying those missing structures would create a new computational problem rather than mirror a result here.
There is a real limit to this negative case: the paper cannot rule out a useful continuous theory of crowd selection. But no such theory is established by its results, and calling a future model a mirror would stretch the term past its anchor. The defensible conclusion is narrower than “no worthwhile scenario exists”: this paper offers no computational result for ChoCo to continuize.
The adversarial triple: the proponent anchors on up to three named results; the opponent sees that case and must defeat every anchor; the judge decides which case convinced it. These are the pipeline’s own outputs, generated by tools/triple_run.py — no human edited them. The paper’s own text is not reproduced here beyond the quoted statement above.