| paper | d4-conf-aaai-2019-0802 |
| 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 is a survey that summarizes methods from other works; it establishes no numbered computational result of its own. With no eligible result to anchor, a continuous task-allocation model would be a new project rather than a mirror of this paper.
fails bit a — no named computational result to mirror
The paper surveys truth inference, data-quality improvement, and prediction-model learning; none is established here as a named computational result.
This paper contains no qualifying named computational result: it has no numbered theorem, lemma, corollary, or proposition asserting a complexity classification or algorithmic result. It is a survey that describes work on truth inference, active learning, and other methods, but those descriptions are not results established by this paper.
So there is no eligible anchor to restate, and I can’t give an anchor-based continuous problem under your rules. One could propose grouping workers by reliability or tasks by difficulty and studying mass-based inference or label selection, but that would be a new research question, not a mirror of a named result here. This leaves the existence of a possible mirror open; it means this paper cannot support the requested positive case.
The proponent raised no qualifying anchor, so there is no continuous question to defeat one by one. That is decisive for this paper: it is a survey, and it proves no named computational result whose complexity or algorithmic question can be mirrored. Its descriptions of truth inference, active learning, and prediction methods report work from other papers; they do not establish results of this paper.
The strongest repair would be to build a continuous model of a large annotator pool, with worker types defined by reliability and cost, and study mass allocation across task types. That could be a sensible new computational project. It still has no result in this survey to mirror: it changes the question into population-level task allocation. So I would vote against a continuous mirror of this paper under the programme’s result-anchored brief. That verdict does not rule out continuous crowdsourcing research in its own right.
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.