Peer production, 혹은 개인 생산(혹은 이용자 생산)과 관련해 지적되는 가장 큰 문제는 그 결과물의 퀄러티와 관계된 것입니다. 사실 이 문제에 대한 답은 별로 어렵지 않습니다. 답은, 기존 시장 기반(market-based) 생산 방식, 혹은 광고 기반 생산 방식의 결과물들에 비해 그 퀄러티가 높을 수도 있고 낮을 수도 있다 입니다. 평균적으로는? 대체적으로는? 그건 그리 똑똑한 질문이 아닙니다. 어쩌면, 서로 비교하는 것 자체가 적절하지 않을 수있습니다.

벤클러 교수가 The Green society 예를 가지고 설명했듯이, 사회적 생산 방식이라는 것 자체가 무한히 많은 컨텐츠를 전제로 합니다. 그렇게 무한히 많은 것들 가운데서....대체로, 평균적으로를 따지는 것은 큰 의미가 없습니다. 사실, 인터넷을 조금만 적극적으로 이용해 보면, 광고에 기반하지 않는, 시장에 기반하지 않는, 이윤 획득에 동기화 되지 않은 수많은, 별처럼 무수히 많은 좋은 컨텐츠들을 만날 수 있습니다. 그런 컨텐츠를 정기적으로 접하는 사람들에게는, 사회적 생산물의 퀄러티에 대해 별도의 설명이 필요치 않을 겁니다.

사회적 생산 방식의 결과물과, 시장 기반 생산물을 구분하는 것도 큰 의미가 없습니다. 사회적 생산 방식이란, 최초의 Utterance에서 출발합니다. 그 최초의 Utterance는 시장 기반 생산물 일수도 있고 아닐 수도 있습니다. 지식의 사회적 생산 양식이란.....시장 기반 생산을 포괄하는 것입니다. 시장 기반 생산에서 사회적 생산 양식으로의 이행이란, 시장 기반 생산 양식이 유일한 지식 생산 양식있던 사회에서, 시장 기반 생산 양식이 여러 생산 양식중 하나의 생산양식인 사회로 이행하는 것을 말합니다.    

질문해 보겠습니다. 유튜브의 컨텐츠가 KBS 컨텐츠 보다 어떻습니까? 질적으로 못합니까? 아니면 더 좋습니까?

말이 안되는 질문이죠...유튜브에는 KBS 컨텐츠도 있을 수 있거든요. KBS 뿐 아니라 전세계의 컨텐츠가 다 있습니다.  TV 컨텐츠만 있는 것이 아니죠. 카우프만의 강의도 있고, 월러스틴의 인터뷰도 있습니다. 정신병자의 중얼거림도 물론 있겠죠. 

사실 이용해 보면.....별 설명이 필요 없죠. 설명은 이용하지 않는 사람들을 위해 필요한 겁니다. 그래서 조금은, 진부한 느낌이 있기도한데.... 벤클러 교수가 그의 책, "The wealth of networks"  3장에서 나름대로 친절히, 수고를 아끼지 않고 설명해 주고 있습니다. 그는 퀄리티를 직접 설명하는 대신, 퀄리티 평가를 구성하는 두가지 요소 즉 Relavance(연관도)와 Accreditation(공신도)에 촛점을 맞추고  그것이, 사회적 생산양식에서 어떻게 검증되고, 선별되고, 획득되는 지를 설명하고 있습니다.

솔직히 사실 머, 그렇게 새로운 내용은 아닙니다. 이미, 잘 알려져 있고, 우리나라에선 이미 다 하고 있는 것이기도 하니까요... 그래도, 기본 머커니즘을 정리하는 겸해서, 또 혹시 새로운 ideation이 가능할 지도 모르니...

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Can relevance and accreditation itself be produced on a peer-production model?

One type of answer is provided by looking at commercial businesses that successfully break off precisely the "accreditation and relevance" piece of their product, and rely on peer production to perform that function.

Amazon and Google are probably the two most prominent examples of this strategy.

Amazon uses a mix of mechanisms to get in front of their buyers of books and other products that the users are likely to purchase.

A number of these mechanisms produce relevance and accreditation by harnessing the users themselves.

At the simplest level, the recommendation "customers who bought items you recently viewed also bought these items" is a mechanical means of extracting judgments of relevance and accreditation from the actions of many individuals, who produce the datum of relevance as by-product of making their own purchasing decisions.

Amazon also allows users to create topical lists and track other users as their "friends and favorites."

Amazon, like many consumer sites today, also provides users with the ability to rate books they buy, generating a peer-produced rating by averaging the ratings.

More fundamentally, the core innovation of Google, widely recognized as the most efficient general search engine during the first half of the 2000s, was to introduce peer-based judgments of relevance.

Like other search engines at the time, Google used a text-based algorithm to retrieve a given universe of Web pages initially.

Its major innovation was its PageRank algorithm, which harnesses peer production of ranking in the following way.

The engine treats links from other Web sites pointing to a given Web site as votes of confidence.

Whenever someone who authors a Web site links to someone else's page, that person has stated quite explicitly that the linked page is worth a visit.

Google's search engine counts these links as distributed votes of confidence in the quality of the page pointed to.

Pages that are heavily linked-to count as more important votes of confidence.

If a highly linked-to site links to a given page, that vote counts for more than the vote of a site that no one else thinks is worth visiting.

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Slashdot case study

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Filtering and accreditation of comments on Slashdot offer the most interesting case study of peer production of these functions.

Users submit comments that are displayed together with the initial submission of a story.

Think of the "content" produced in these comments as a cross between academic peer review of journal submissions and a peer-produced substitute for television's "talking heads."

It is in the means of accrediting and evaluating these comments that Slashdot's system provides a comprehensive example of peer production of relevance and accreditation.

Slashdot implements an automated system to select moderators from the pool of users.

Moderators are chosen according to several criteria; they must be logged in (not anonymous), they must be regular users (who use the site averagely, not one-time page loaders or compulsive users), they must have been using the site for a while (this defeats people who try to sign up just to moderate), they must be willing, and they must have positive "karma."

Karma is a number assigned to a user that primarily reflects whether he or she has posted good or bad comments (according to ratings from other moderators).

If a user meets these criteria, the program assigns the user moderator status and the user gets five "influence points" to review comments.

The moderator rates a comment of his choice using a drop-down list with words such as "flamebait" and "informative."

A positive word increases the rating of a comment one point and a negative word decreases the rating a point.

Each time a moderator rates a comment, it costs one influence point, so he or she can only rate five comments for each moderating period.

The period lasts for three days and if the user does not use the influence points, they expire.

The moderation setup is designed to give many users a small amount of power.

This decreases the effect of users with an ax to grind or with poor judgment.

The site also implements some automated "troll filters," which prevent users from sabotaging the system.

Troll filters stop users from posting more than once every sixty seconds, prevent identical posts, and will ban a user for twenty-four hours if he or she has been moderated down several times within a short time frame.

Slashdot then provides users with a "threshold" filter that allows each user to block lower-quality comments.

The scheme uses the numerical rating of the comment (ranging from -1 to 5).

Comments start out at 0 for anonymous posters, 1 for registered users, and 2 for registered users with good "karma."

As a result, if a user sets his or her filter at 1, the user will not see any comments from anonymous posters unless the comments' ratings were increased by a moderator.

A user can set his or her filter anywhere from -1 (viewing all of the comments) to 5 (where only the posts that have been upgraded by several moderators will show up).

Relevance, as distinct from accreditation, is also tied into the Slashdot scheme because off-topic posts should receive an "off topic" rating by the moderators and sink below the threshold level (assuming the user has the threshold set above the minimum).

However, the moderation system is limited to choices that sometimes are not mutually exclusive.

For instance, a moderator may have to choose between "funny" (+1) and "off topic" (-1) when a post is both funny and off topic.

As a result, an irrelevant post can increase in ranking and rise above the threshold level because it is funny or informative.

It is unclear, however, whether this is a limitation on relevance, or indeed mimics our own normal behavior, say in reading a newspaper or browsing a library, where we might let our eyes linger longer on a funny or informative tidbit, even after we have ascertained that it is not exactly relevant to what we were looking for.

The primary function of moderation is to provide accreditation.

If a user sets a high threshold level, they will only see posts that are considered of high quality by the moderators.

Users also receive accreditation through their karma.

If their posts consistently receive high ratings, their karma will increase.

At a certain karma level, their comments will start off with a rating of 2, thereby giving them a louder voice in the sense that users with a threshold of 2 will now see their posts immediately, and fewer upward moderations are needed to push their comments even higher.

Conversely, a user with bad karma from consistently poorly rated comments can lose accreditation by having his or her posts initially start off at 0 or -1.

In addition to the mechanized means of selecting moderators and minimizing their power to skew the accreditation system, Slashdot implements a system of peer-review accreditation for the moderators themselves.

Slashdot accomplishes this "metamoderation" by making any user that has an account from the first 90 percent of accounts created on the system eligible to evaluate the moderators.

Each eligible user who opts to perform metamoderation review is provided with ten random moderator ratings of comments.

The user/metamoderator then rates the moderator's rating as either unfair, fair, or neither.

The metamoderation process affects the karma of the original moderator, which, when lowered sufficiently by cumulative judgments of unfair ratings, will remove the moderator from the moderation system.

 

Together, these mechanisms allow for distributed production of both relevance and accreditation.

Because there are many moderators who can moderate any given comment, and thanks to the mechanisms that explicitly limit the power of any one moderator to overinfluence the aggregate judgment, the system evens out differences in evaluation by aggregating judgments.

It then allows individual users to determine what level of accreditation pronounced by this aggregate system fits their particular time and needs by setting their filter to be more or less inclusive.

By introducing "karma," the system also allows users to build reputation over time, and to gain greater control over the accreditation of their own work relative to the power of the critics.

Users, moderators, and metamoderators are all volunteers.

The primary point to take from the Slashdot example is that the same dynamic that we saw used for peer production of initial utterances, or content, can be implemented to produce relevance and accreditation.

Rather than using the full-time effort of professional accreditation experts, the system is designed to permit the aggregation of many small judgments, each of which entails a trivial effort for the contributor, regarding both relevance and accreditation of the materials.

The software that mediates the communication among the collaborating peers embeds both the means to facilitate the participation and a variety of mechanisms designed to defend the common effort from poor judgment or defection.

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Social networking media가 사용하는 다양한 Accrediation과 relevance 시스템에 대해 정리해보는 것이 필요할 것 같네요. 각각의 장단점 등을 비교할 수있도록 말입니다.

그리고, 밴클러 교수의 꼼꼼한 설명을 읽다가 불현듯 든 생각......

특정 목적에 부합하도록, Accrediation과 relevance 시스템을  설계, 디자인 하는 작업이 매우 흥미롭고, 도전적이면서도 고급스러운 업무라는.....

그리고 그런것을 전문으로 하는 컨설팅도 가능하겠다는....  


관련 포스트
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  Modularity & Granularity
  Yochai Benkler의 "The Networked Information Economy" 
  The Reds, the Blues, and the Greens-Benkler
  저작권, 사회적 생산, 그리고 동기에 관한 Benkler의 견해
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