Against Security: How We Go Wrong at Airports, Subways, and Other Sites of Ambiguous Danger

Harvey Molotch

How security procedures could be positive, safe, and effective The inspections we put up with at airport gates and the endless warnings we get at train stations, on buses, and all the rest are the way we encounter the vast apparatus of U.S. security. Like the wars fought in its name, these measures are supposed to make us safer in a post-9/11 world. But do they? Against Security explains how these regimes of command-and-control not only annoy and intimidate but are counterproductive. Sociologist Harvey Molotch takes us through the sites, the gizmos, and the politics to urge greater trust in basic citizen capacities—along with smarter design of public spaces. In a new preface, he discusses abatement of panic and what the NSA leaks reveal about the real holes in our security.

Politics & GovernmentPolitics & Government · medium confidencePolitics & Government81 signalsLlm Classifier: Politics & Government -> Politics & GovernmentGoogle Books: Political Science -> ScienceGoogle Books: Political Science -> Politics & GovernmentMoney, Markets & Economic PolicyEmpire & Colonialism
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

  2. 02 Central places

    None extracted with sufficient confidence.

  3. 03 Suggested argument

    Security inspection regimes at airports and transit sites are counterproductive—often intimidating and ineffective—so public safety requires smarter design of spaces and greater trust in citizens’ capacities rather than command-and-control procedures.

    Model confidence 62%

  4. 04 Reading orientation

    64 / 100 academic

    Academic

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 74%.