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EAN-139780804759014   EAN-13 barcode 9780804759014
Product NameAgnotology: The Making And Unmaking Of Ignorance
LanguageEnglish
CategoryBook / Magazine / Publication
Amazon.comA Buy on Amazon ~ 0804759014
SKUNU-ING-00474117
Price New18.86 US Dollars    (curriencies)
Price Used13.23 US Dollars    (curriencies)
Width0.9 inches    (convert)
Height9 inches    (convert)
Length6 inches    (convert)
Weight15.04 ounces    (convert)
Page Count312
BindingPaperback
Published05/12/2008
FeaturesUsed Book in Good Condition
Long DescriptionWhat don't we know, and why don't we know it? What keeps ignorance alive, or allows it to be used as a political instrument? Agnotology―the study of ignorance―provides a new theoretical perspective to broaden traditional questions about "how we know" to ask: Why don't we know what we don't know? The essays assembled in Agnotology show that ignorance is often more than just an absence of knowledge; it can also be the outcome of cultural and political struggles. Ignorance has a history and a political geography, but there are also things people don't want you to know ("Doubt is our product" is the tobacco industry slogan). Individual chapters treat examples from the realms of global climate change, military secrecy, female orgasm, environmental denialism, Native American paleontology, theoretical archaeology, racial ignorance, and more. The goal of this volume is to better understand how and why various forms of knowing do not come to be, or have disappeared, or have become invisible.
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Article of interest

Here we will demonstrate the most basic example of importing the CSV data files that we produce on this site into your MySQL database.

For information about various databases you can use and how to import CSV files into them, please view the overview article "Importing CSV data into your database".

For this example, we are going to import the product data CSV file out of the sample_ean_data.zip but this same process will work on the full data download file. We will also be executing the commands in the MySQL Workbench but you can also use the command line tool with the same commands if you like.

First, start by creating a blank table. Use the table layout described in the read_me file for the most up-to-date table layout. It is suggested that you not use any indexing at this point. You can add indexes later. It is most likely that you will have your own tables where you want to store your data so importing the CSV files can be done into temporary tables and then later copied over to your tables. Leaving off the indexes and constraints on these import tables reduces the risk of import errors. Here is an example:

create table ean_product
(
    EAN13             varchar(13),
    UPCA              varchar(12),
    UPCE              varchar(8),
    SKU               varchar(200),
    PriceNew          numeric(15,2),
    PriceUsed         numeric(15,2),
    PriceDate         date,
    company           varchar(13),
    product           varchar(100),
    description       varchar(100),
    category          int,
    url               varchar(500),
    created           datetime,
    modified          datetime
);

Next we perform the import using the LOAD DATA INFILE command. The path to the file depends on where you saved the data and which operating system you are on. For Windows users you might find your file on the C: drive and Linux users may find your date in your home (~) folder. This example shows a Linux import. Only the path would be different between the operating systems.

LOAD DATA LOCAL
    INFILE '~/sample_ean_data/sample_ean_product.csv' 
    INTO TABLE ean_product
    FIELDS TERMINATED BY ',' ENCLOSED BY '"' ESCAPED BY '\\'
    LINES TERMINATED BY '\r\n'
    IGNORE 1 LINES;

Finally, lets look at the data that we just imported.

SELECT * FROM EAN_PRODUCT;

You may have seen some warnings after the import command. If you are concerned about these warnings, examine the data. It could be that some data has grown beyond the size specified in the read_me file. If you are worried, make the fields larger and try the process again after deleting all of the data out of the table.

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