Reddit Reddit reviews Against Method

We found 6 Reddit comments about Against Method. Here are the top ones, ranked by their Reddit score.

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6 Reddit comments about Against Method:

u/restricteddata · 30 pointsr/AskHistorians

So this is a very, very, very tricky question, because when we get right down to it, we still don't have a very rigorous definition of "science" today. That is, we don't have a clear way to say, "this is science" and "this is not science." This is known as the Demarcation problem and after several decades of no progress made, most historians and philosophers of science have simply abandoned the project altogether as a badly thought-out one, even in the cases of outright silly nonsense.

(Now I know a lot of people out there who don't study this stuff for a living are probably saying, but what about Karl Popper? What about falsifiability? Etc. Let me just say that it doesn't really work out very smoothly along those lines and that has been known for many decades now. Falsifiability is a nice way to attack Creationism but as a rigorous means of sorting out science from non-science it falls flat when you start trying to apply it widely.)

It gets much worse if we take philosophical standards of the day (be they Popper's or Merton's or whomevers) and try to apply them backwards in time. We find that most of those heralded as the "first" or "great" scientists break ever rule in the book, routinely. (Galileo is such an offender that Paul Feyerabend wrote an entire book about it.)

So this gets tricky as an historical question, and historians of science are prone to debate with each other just how unclear it is that there, for example, was any kind of "Scientific Revolution" ("There was no such thing as the Scientific Revolution, and this is a book about it.") at all, or whether the evolving professionalization, practices, and mindsets were something both more gradual and as-of-yet-still-unfinished than most people realize.

But that's probably not the answer you're interested in. I think what you're probably going for is a history of professionalization of science, the latter loosely defined as systematic inquiry into nature.

Peter Dear, an historian of science at Cornell, has argued quite persuasively in my mind that the real distinguishing feature of the "Scientific Revolution" of the 15th-16th century (e.g. Galileo et al.) is not that they came up with brand new ways of thinking about the universe, or that Galileo himself was any kind of real outlier here (he did not pop out of nowhere and there were, indeed, plenty of other astronomers and philosophers and etc. running around at the same time as him, though we tend to ignore them), but that they started on a very regular basis merging quantitative studies of nature with philosophical ideas about nature. That is, they started integrating mathematics into their empirical observations, and using these to develop better theoretical models for big questions like "how is the universe run." That, he argues, is somewhat different than what came before, though even then, there are always antecedents. But there are plenty who would even disagree and argue with him on that apparently simple point.

If you want to talk about the professionalization of this kind of inquiry, the early 18th century is when it starts to really become considered almost a "profession" in some parts of the world.

If you want to ask, when does it start to look like what we would today call "science" — with the university positions, industrial cooperation, little boys (and later, girls) saying "daddy I'd like to be a scientist when I grow up," foundations giving grants, people having regular educational and career paths, not just something for rich elites, research published in journals, etc. — that's the mid-to-late 19th century. Obviously bits and pieces of that are present earlier, but prior to the 19th century it still looks, largely, like an informal thing that mostly is done by rich men in their spare time.

Sorry for such a long answer that is probably not what you wanted! I hope, at the minimum, it impresses upon you the fact that historians of science consider this to be a not very easy question to answer, and generally regard the flip answers provided by scientists ("Galileo! Newton!") as being horribly inadequate, if not outright propaganda of a sorts.

u/AgnosticKierkegaard · 9 pointsr/changemyview

So you think you can argue by giving me links to a book on amazon.com? And, its not such an open and shut case as you'd hope, and I highly doubt this solves the problem of induction. I'm not going to argue this here, but I think if you're going to link to a book on amazon I'm entitled to link to another.

http://www.amazon.ca/Enquiry-concerning-Human-Understanding/dp/0199549907/ref=sr_1_1?s=books&ie=UTF8&qid=1398018671&sr=1-1&keywords=hume

And because I'm a jackass. one more

http://www.amazon.com/Against-Method-Paul-Feyerabend/dp/1844674428/ref=sr_sp-atf_title_1_1?ie=UTF8&qid=1398018826&sr=8-1&keywords=against+method

u/CSMastermind · 4 pointsr/learnprogramming

I've posted this before but I'll repost it here:

Now in terms of the question that you ask in the title - this is what I recommend:

Job Interview Prep


  1. Cracking the Coding Interview: 189 Programming Questions and Solutions
  2. Programming Interviews Exposed: Coding Your Way Through the Interview
  3. Introduction to Algorithms
  4. The Algorithm Design Manual
  5. Effective Java
  6. Concurrent Programming in Java™: Design Principles and Pattern
  7. Modern Operating Systems
  8. Programming Pearls
  9. Discrete Mathematics for Computer Scientists

    Junior Software Engineer Reading List


    Read This First


  10. Pragmatic Thinking and Learning: Refactor Your Wetware

    Fundementals


  11. Code Complete: A Practical Handbook of Software Construction
  12. Software Estimation: Demystifying the Black Art
  13. Software Engineering: A Practitioner's Approach
  14. Refactoring: Improving the Design of Existing Code
  15. Coder to Developer: Tools and Strategies for Delivering Your Software
  16. Perfect Software: And Other Illusions about Testing
  17. Getting Real: The Smarter, Faster, Easier Way to Build a Successful Web Application

    Understanding Professional Software Environments


  18. Agile Software Development: The Cooperative Game
  19. Software Project Survival Guide
  20. The Best Software Writing I: Selected and Introduced by Joel Spolsky
  21. Debugging the Development Process: Practical Strategies for Staying Focused, Hitting Ship Dates, and Building Solid Teams
  22. Rapid Development: Taming Wild Software Schedules
  23. Peopleware: Productive Projects and Teams

    Mentality


  24. Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency
  25. Against Method
  26. The Passionate Programmer: Creating a Remarkable Career in Software Development

    History


  27. The Mythical Man-Month: Essays on Software Engineering
  28. Computing Calamities: Lessons Learned from Products, Projects, and Companies That Failed
  29. The Deadline: A Novel About Project Management

    Mid Level Software Engineer Reading List


    Read This First


  30. Personal Development for Smart People: The Conscious Pursuit of Personal Growth

    Fundementals


  31. The Clean Coder: A Code of Conduct for Professional Programmers
  32. Clean Code: A Handbook of Agile Software Craftsmanship
  33. Solid Code
  34. Code Craft: The Practice of Writing Excellent Code
  35. Software Craftsmanship: The New Imperative
  36. Writing Solid Code

    Software Design


  37. Head First Design Patterns: A Brain-Friendly Guide
  38. Design Patterns: Elements of Reusable Object-Oriented Software
  39. Domain-Driven Design: Tackling Complexity in the Heart of Software
  40. Domain-Driven Design Distilled
  41. Design Patterns Explained: A New Perspective on Object-Oriented Design
  42. Design Patterns in C# - Even though this is specific to C# the pattern can be used in any OO language.
  43. Refactoring to Patterns

    Software Engineering Skill Sets


  44. Building Microservices: Designing Fine-Grained Systems
  45. Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools
  46. NoEstimates: How To Measure Project Progress Without Estimating
  47. Object-Oriented Software Construction
  48. The Art of Software Testing
  49. Release It!: Design and Deploy Production-Ready Software
  50. Working Effectively with Legacy Code
  51. Test Driven Development: By Example

    Databases


  52. Database System Concepts
  53. Database Management Systems
  54. Foundation for Object / Relational Databases: The Third Manifesto
  55. Refactoring Databases: Evolutionary Database Design
  56. Data Access Patterns: Database Interactions in Object-Oriented Applications

    User Experience


  57. Don't Make Me Think: A Common Sense Approach to Web Usability
  58. The Design of Everyday Things
  59. Programming Collective Intelligence: Building Smart Web 2.0 Applications
  60. User Interface Design for Programmers
  61. GUI Bloopers 2.0: Common User Interface Design Don'ts and Dos

    Mentality


  62. The Productive Programmer
  63. Extreme Programming Explained: Embrace Change
  64. Coders at Work: Reflections on the Craft of Programming
  65. Facts and Fallacies of Software Engineering

    History


  66. Dreaming in Code: Two Dozen Programmers, Three Years, 4,732 Bugs, and One Quest for Transcendent Software
  67. New Turning Omnibus: 66 Excursions in Computer Science
  68. Hacker's Delight
  69. The Alchemist
  70. Masterminds of Programming: Conversations with the Creators of Major Programming Languages
  71. The Information: A History, A Theory, A Flood

    Specialist Skills


    In spite of the fact that many of these won't apply to your specific job I still recommend reading them for the insight, they'll give you into programming language and technology design.

  72. Peter Norton's Assembly Language Book for the IBM PC
  73. Expert C Programming: Deep C Secrets
  74. Enough Rope to Shoot Yourself in the Foot: Rules for C and C++ Programming
  75. The C++ Programming Language
  76. Effective C++: 55 Specific Ways to Improve Your Programs and Designs
  77. More Effective C++: 35 New Ways to Improve Your Programs and Designs
  78. More Effective C#: 50 Specific Ways to Improve Your C#
  79. CLR via C#
  80. Mr. Bunny's Big Cup o' Java
  81. Thinking in Java
  82. JUnit in Action
  83. Functional Programming in Scala
  84. The Art of Prolog: Advanced Programming Techniques
  85. The Craft of Prolog
  86. Programming Perl: Unmatched Power for Text Processing and Scripting
  87. Dive into Python 3
  88. why's (poignant) guide to Ruby