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Ideas for a UO Sustainability Conference in October?

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Steve Mital, Sustainability Director for the University of Oregon, recently sent a call for ideas to help guide a Sustainability Conference tentatively planned for the 23rd and 24th of October, 2008.  It is being organized by Sustainability Directors at Portland State University, Oregon State University, and the University of Oregon, and the second day will reportedly be “entirely devoted to students and sustainability.”  My suggestions for the conference, written in full on the Oregon Direct Action blog, revolve around these ideas:

  • Planning this conference digitally and in the public eye so that students can be a part of the entire process
  • Adding an international component to help bridge the local-international sustainability gap
  • Networking with local sustainability non-profits
  • Drafting a set of sustainability guidelines for campus community to voluntarily adopt (i.e. minimizing paper use, using Tupperware instead of styrofoam, etc.)
They are looking for ideas on “workshops, themes, keynote speakers, etc.” until July 3rd.  Let’s make this conference worth attending!

ATSG technical update

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One aspect of Whitman Direct Action’s (WDA) 2007-2008 Sadhana Clean Water Project is the Appropriate Technology Study Group, looking at the socio-political constraints to clean water access in the Kolwan Valley of southern India. Traditionally, WDA has been an implementing organization, generally working with an in-country, non-governmental organization (NGO) to bring a piece of technology to a community or region. In early conversations with one of our collaborating partners, Sadhana Village, we determined it would be more poignant to rather work to understand why water access projects aren’t adopted to the degree hoped, and establish some of the challenges they face.

The Kolwan Valley [Google Maps], where we conducted our research, is an area an hour drive from Pune. It is comprised of 17 villages, or 19 if you count the larger Paud [Google Maps] and Kolwan [Google Maps]. The large majority of households earn their income through subsistence farming, with wheat and sugarcane being the primary crops, and everyone else through a small variety of other means. At this point in time, there is almost zero industry in the valley. This could soon change because of the proximity to a rapidly expanding urban center (Pune). Village size is generally between 70 and 400 households, which are then commonly split into between two and five “wadis” or pockets including the village proper. Composition of the wadis is, for the most part, determined by socio-economic background; for instance, in many of the villages we worked in, there was a “harijan vasti” scheduled caste (SC) families. Governance is done on a local level by the Gram Panchayat, a “democratically” elected body responsible for the basic issues of each village, and on a wider scale by the Gram Sevaks and regional Indian government. The structure of these villages, and of the valley, is as such to provide characteristics unique to the area and threaded throughout India.

Data collection done on the ground by participants in the study group consisted first of surveys coordinated by two partners, Mahindra United World College of India and Gomukh Environmental Trust, and implemented by high-school students of both MUWCI and the valley. Over two hundred responses from nearly all of the villages were collected. A second, preplanned component of the research was a series of focus groups and/or discussions with different types of groups from the valley, including scheduled caste women, school children, and the Block Development Officer (BDO), an official responsible for the government-sponsored water management projects. With one of our goals being to collect information on the same topics related to water availability, water quality, water quantity, and sanitation from different stakeholders, we found it was also wise to interview some member of the Gram Panchayat to get an “official” view of those aspects in each village. This detailed information on where certain wadis get their water, how much they get, and so on proved to be crucial in determining which water sources, or points of distribution, we should test.

Our guiding focus for the Water Quality and Quantity Addendum was originally to determine whether the water in the valley is generally safe to drink or not without further treatment, as well as to collect the supplementary data to establish a need for better water management. One reason for this is to partially substantiate the report produced by the study group; it will be important when we pen the paper to prove there are both socio-political constraints in the region and that the valley has a water problem to begin with. Although much of this type of information should be available from the Indian government, we decided, with more explanation later, to go ahead and do independent testing of the basic indicators of water quality:

  • pH
  • Temperature (C)
  • Fecal coliform
  • Turbidity (NTU)
  • Dissolved oxygen (mg/L)
  • Total nitrate and nitrite (mg/L)
  • Biochemical Oxygen Demand (mg/L)
  • Chloride (mg/L)

The tests were done through a variety of means. Dissolved Oxygen, pH, and temperature were all done in the field, as well as total nitrate, nitrite, and chlorine when the Lifewater kits showed up, and we took samples for the rest. On returning to the lab, the water for fecal coliform tests was placed on a culture of McConkey’s Agar for 24 to 48 hours. They were then assessed for growth of lactose and peptone-producing colonies, indicative of E. Coli, Salmonella, and other bacteria potentially harmful to human health. The pros and cons of each testing method will be documented in the full report.

As with working in any foreign country, there were, and still are, many challenges to getting the necessary hard data required for such a report. A significant amount of time, anywhere between one and four hours per village depending on how many cups of chai forced upon the team, was required to do a Village Water Source Worksheet [HTML], the first step towards understanding where we should test. Another timesink was that each one of these worksheets required at least one and sometimes two or three translators. This can easily magnify the amount of time needed as a question must first bounce from person to person and then the answer back the same path. One justification for why these questions have been necessary is that reliable information from the government is notoriously difficult to get, in both time consumed and accuracy. For many complex reasons, very basic data on water quality, quantity, and access sometimes either does not exist or is falsified. On top of that, there is an extraordinary bureaucracy to work through in order to obtain stats. The first person you talk to will pass you on to the next, and so on and so forth.

Regardless of these difficulties, we were still able to test 15 points in 11 villages assessed.

By testing for basic indicators of water quality, and surveying for hard data about the water sources in each village, we will be able to establish far more than just whether the water is generally safe to drink or not without further treatment. For instance, determining whether there is a presence of fecal coliform in the water can validate the accuracy of statements on both how often the water is treated and tested. If the man in charge of treating the water says he puts TCL in every day, but there is bacterial growth in the sample taken, there we will be able to document that there is a disconnect somewhere along the line. Furthermore, if the water source is being tested regularly, and there are indicators that the water is unsuitable for human drinking, then there should be action by the local and regional government to correct the problem. A presence of bacterial growth in the water could indicate some breakdown in the societal mechanisms required to provide safe drinking water. It is in ways like these that the hard data we’ve collected on the ground is proving to be a valuable asset.

With all of that being said, a fair bit of work still needs to be done. The collection of raw data from the Appropriate Technology Study Group is only just now being synthesized for analysis; through this project, we’ve been able to come to the overall conclusion that data collection is a time-consuming process. If it is at all possible, we would like to obtain the official water quality data from the government to see how it compares to our information, as well as use it to describe the long-term trends of the valley. It’s accuracy, of course, would have to be taken with a grain of salt. We made a request for this information to the BDO a couple of weeks back, and promised we could get it, but it has yet to come. It will also be important to continue tracking down the appropriate climate and water availability information to be able to compare how much water villagers perceive there to be compared to how much there actually is in each season, in addition to being used to depict the characteristics of the valley. Furthermore, it could be interesting to get hard data on how much water is being used for what, including what quantity is diverted away from the valley for use in Pune. The other data required to support certain arguments in the report will likely arise as we continue to figure out which specific dynamics in the Kolwan Valley inhibit access to clean and reliable drinking water.

An abandoned lighthouse?

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At the ad:tech conference this year in New York City, the most widely anticipated news came from a company less than three years old. This is hardly a surprise to those who follow the tech industry; Facebook, currently valued at over 15 billion dollars, is the hottest thing since Google or MySpace. It has been on the radar along with Apple’s iPhone as one of the biggest stories of the year. Accordingly, the first announcement of how the social network is going to monetize its service, a problem plaguing every Web 2.0 startup, set the blogosphere aflame. Facebook’s name for its new ad marketing platform: Beacon.

The origin

Targeted advertising isn’t anything new. It’s only natural a business would want to pitch its product to the audience most likely to buy it. Time spent on a consumer who isn’t going to be a buyer is simply a wasted effort. Selling the merits of a men’s cologne to pre-teen girls isn’t effective just like pitching hearing aids to twenty-somethings with perfect hearing is a waste.  It pays to focus advertising as directly as possible; in financial terms, it minimizes the dollars spent selling to each consumer while maximizing the company’s overall profits.

In 1932, Young and Rubicam became the first firm to advertise based on statistics. Twenty years later, the A.C. Nielsen Market Research Company, realizing the extraordinary potential of television to reach a mass audience, began tracking which prime-time shows were being watched in what types of households. As technology progressed, so did the sophistication of the ability to track viewers and their habits; by the 1970’s, tracking services could report many more details about audiences including race, gender, age, and educational background. Personalized advertising started crawling on its hands and feet.

Jump forward another twenty years to the commercial advent of the Internet. Its digital nature allows for inherently easier tracking. While transferring data back and forth, the web requires unique electronic addresses to ensure the bits requested make it to the correct recipient. This characteristic also means a digital “paper trail” is left in every transaction. Capitalizing on this technology, web metrics have advanced to a point where a nearly infinite amount of consumer information can be aggregated and analyzed. The current difficulty, if it can be summarized, lies in determining which information is most important and how it should be interpreted.

Problems to some are opportunities to others. One burgeoning market is online advertising, with has had over 150% growth in revenues since 2000. Success in this arena is defined by the businesses who achieve the highest conversion rates; it’s what has made Google the 5th largest company in the United States in less than a decade.

There are now a few common ways of using consumer metrics to target advertisements online.  One method, borrowed from the print media, is selling advertising space based on the perceived reader demographics of a website. Grist, an environmental news nonprofit, and The Economist, a business and political analysis publication, both do this for placements on their websites. Making the deals in-house, albeit a significant amount of work, does have some added benefits. The most significant include being able to target to a specific demographic and using richer media (e.g. images and video) in advertisements. Google’s AdSense, on the other hand, is an example of a newer, content-based approach to delivering advertisements.  Known abstractly as “contextual advertising,” it optimizes ad placement by analyzing the content of the website and listing the only most relevant promotions. Doing this by looking at topics, keywords, and phrases pretty well guarantees that the text-based advertisement will be on line with the focus of the site. Yet, at the same time, those ads lose efficacy when readers learn how to ignore them.

So begins the cat and mouse game.

Facebook, by capitalizing on the social graph between its users, is making advertising “social.” Originally exclusive to college students, this social network hasn’t been without its controversial business decisions. One such event, the launch of a tool called the “News Feed” which is designed to aggregate friends’ activities on the site, caused users to go up in virtual arms about privacy concerns. A mass exodus was only averted after the founder, Mark Zuckerberg, published an open letter promising to alleviate those worries. He might have to do this again.

Unlike Google’s AdSense, which advertises based off contextual data, Facebook now has two advertising platforms which exploits the social data its users provide: Social Ads and Beacon. Social Ads places advertisements for sponsored businesses and products in the sidebar and previously controversial News Feed. These placements are targeted based on information from a user’s profile; for instance, having “photography” listed as interest in the personal section will incur a higher than normal number of ads for photo contests or camera equipment. The other system, Beacon, works by through a hybridization of “viral marketing.” When a user buys a product on an affiliated site, the information gets sent back to Facebook and is placed in the News Feed of another user. The idea, or at least in theory, is that the advertisers gain traction through a “forced word-of-mouth.” Facebook hopes to make this possible with their platform, although users haven’t been so happy about it.

Personalization is in the future of advertising. AdSense, Beacon and others are only the forerunners in a continual evolution of marketing directly to a consumer. Take, for instance, a product such as Google Maps. In the past year, Google has introduced sponsored, location-based results when a user types in a query like, “pizza portland oregon.” With the launch of Google’s Android Mobile OS in the next year, Google Maps will be available on a number more handheld devices. Add a GPS-enabled wireless device into the mix and the user will no longer have to type in the “portland oregon.” Google will know, thanks to technology. Thanks to technology, advertising too will become more targeted in every way; based on location through GPS, based on past purchases with online retailers, and based the personal interests listed on social profiles.

Or at least that’s the current trend of thinking.

Some implications

Privacy. A world where information about an individual’s actions flow freely to businesses leave little maneuvering room for a personal life. Transparency should be a two-way street. Consumers need to critically assess how much privacy they are willing to give up, and to whom they want to give it to. In the case of Beacon, the platform has become so disputed that is has attracted the attention of MoveOn.org, a civic action organization normally focused on politics. As part of a multi-pronged approach, the nonprofit created a Facebook Group titled, “Petition: Facebook, stop invading my privacy!” and draws upon members to be activists. Their intent is to call upon the company for a public response to an issue which has created headlines such as, “Does Facebook Hate Christmas?,” “Is Facebook a Privacy Nightmare?” and “Are Facebook’s Social Ads Illegal?” With enough voices, and media publicity, the tactic is sure to be successful; Facebook, unless interested in committing financial suicide, has no interest in causing the entire core of its business model to migrate to another social network. What the long-term loss, or gain, to user privacy is, however, has yet to be decided.

Integrity. The effect advertising has on content is also a very important question. In a world where it is becoming the easier choice to monetize a business with paid advertising, one must ask what sort of effect such as decision has on independence. Take journalism, for instance. Although this model is not yet entirely true of major papers, many blogs write journalistically, are supported by advertisers, and have become primary sources for niche news. Without an established and transparent code of ethics, it is impossible to guess at the editorial integrity of a website. Some naive audiences assume their authority, but every reader must be a critical reader and look at the policies behind their business practices. Grist and The Economist, for instance, have advertising policy links on top of clearly defined ads. Some sites running Google AdSense, conversely, embed their advertisements in the content of the page or in faux navigation bars. An uneducated visitor, subsequently, does not know the different between what is real and what is advertisement. For the integrity of journalism, and of all media, there needs to be a clear line between independent content and advertising.

In an economy increasingly dependent on universal participation, it doesn’t pay to exploit user data. Using those same crowds to deduce such a decision, however, is a smart choice to make.

Written for the final paper in J 201 Mass Media and Society. Also available to download in PDF