Capstone: Fallacy Hunt
This video presents the same text shown beside it, spoken and on screen. It adds nothing the text does not say.
This capstone reads one editorial straight through, then dissects it — tagging each error by name, and acquitting one move that only resembles a fallacy.
The editorial argues against a proposed cycle lane. Read it once at speed, then go back. First move: the councillor backing the lane cycles to work, so naturally she supports it. That attacks the advocate's motive rather than the proposal, and the near-miss check fails — her commute does not bear on whether the lane reduces collisions. Tagged. Second move: if we approve this lane, next it will be car-free streets, then a ban on driving altogether. Each step is asserted rather than supported, and no mechanism links them. A slippery slope, tagged. Third move: either we keep the parking or we lose the high street. Two options are presented as exhaustive when reduced parking with better transit is a live third. False dilemma, tagged. Fourth move: nobody in the neighbourhood wants this. The claim is offered without a survey, and the phrasing makes any resident who does want it not count as being from the neighbourhood. Tagged twice — an unsupported claim and a definition rigged to protect itself. Fifth move: the city's traffic engineers modelled the junction and project a small delay increase. This one gets acquitted. It cites relevant expertise inside its own field on a question that field settles, and the fact that it supports the writer's side does not make it fallacious. Final count: four errors and one honest appeal.
The acquittal carries as much weight as the convictions. An analysis that finds a fallacy in every paragraph has stopped measuring the argument and started matching shapes, and it is the acquittal that shows the method was actually applied.
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