A War Of Two Worlds

A War of Two Worlds – Part 1 In this, Part I of a three part article, the author presents a new method to evaluate the potential effectiveness of Op Ed or other expository writings. Part 2 considers the barriers faced by activists as they attempt to influence citizen motivation to participate in the political process. Part 3 looks at the targets of policy change, the Politicians, and the potential for changing their behavior.

Introduction With hundreds of Op Ed articles submitted each week to OEN and many more to similar sites, there appears to be no mechanism to evaluate the likelihood that submitted articles will meet their objectives, (persuading readers to adopt the theses advanced by their authors). While OEN has developed system designed to assess some perceived qualitative characteristics of submitted articles, it lacks the clarity of terms, sensitivity and objectivity required to meet this objective. One element in this process is the level of competition for the eyes and ears of the audience. OEN is only one of literally thousands of opinion sites, each competing for their text, audio, or video media offerings. In that competitive environment, an author seeking attention to his work is required to conform to an increasing standard of excellence, lest he be quickly discarded. A central thesis upon which this paper is premised tells us: Op Ed media, in whatever form presented, have a low probability of changing attitudes of their audiences, and when they do, these changes tend to be transient, with audiences returning to originally held views after a short period of time. Two opposing articles, “Lesser Evils,” by J.C. Garrett, [JCG] and Kevin Gosztola’s [KG] Lesser Evils, Psychoses, and the Perils of Voting Your Conscience [Part 1], a rebuttal to the JCG posting, presented a an opportunity to test this hypothesis, using a new procedure for rating the potential effectiveness of Op Ed submissions.

Assessing the Impact of the Op Ed Article In an effort to assess the impact of Op Ed articles on the reader’s voting choices, the author offered a poll designed to answer this question, with respect to the two articles being evaluated. While the number of respondents was insufficient to meet requirements for statistical significance, the trending illustrated in the tables below, was instructive.

By a ratio of 57:43 respondents indicated an intent to vote for Obama. If this ratio is at all representative of OEN members, those planning on voting for 3rd Party candidates, or withholding their votes, is unexpectedly high. But, is it really that high? We have only to look at the disaffected “professional” delegates to the Democratic National Convention, the Clinton supporters, some of whom remain intractable in their decision either to vote for McCain, or not vote. They, of all people, should have the necessary data in hand to make an informed choice. Before attempting to explain this apparent difference between reported and expected behavior, the effect of the Op Ed postings is assessed. As noted in the row percentages, those who reported as being influenced by articles accounted for 21% of respondents. While the total n is small, the difference between those affected choosing to vote for Obama (13%) vs. those making the irrational choice (30%) is additional confirmation of higher than expected irrational selections.

Evaluating the Op Ed Submission The probability that an individual will be persuaded to agree to that advocated by an Op Ed position if clearly a function of two elements; (a) the readiness or susceptibility of the individual to persuasion, and (b) the effectiveness of the presentation of the Op Ed communication. The calculation of (a) is beyond the scope of this discussion, leaving our focus directed at the development of a method for objectively assessing the viability of any Op Ed article. Description of the methodology While many rating systems are employed to evaluate the effectiveness of articles (as a class – no discrimination is made between Op Ed and other categories of postings), all have in common two characteristics. Ratings are entirely subjective. When terms such as “Importance,” “Value,” “Perspective,” are employed, none have sufficiently precise definitions to insure raters use the same criteria for their ratings

Regardless of the clarity of rating terms, the more serious flaw, from the perspective of analysis, is that each rating criterion is applied to the entirety of the piece under consideration. Such an application fails to consider intra-article variations, nor the structure of its elements. Thus, the author offers a new paradigm, applicable to any expository writing. It offers opportunity for raters to apply objective criteria to each sentence to determine, the degree to which it contributes to meeting the overall objective of the article Finally, it allows for a comparison of multiple articles, whether by the same or different authors. For those interested in the mechanics necessary to carry out this analysis, readers are referred to in the White Paper prepared by this author last year. Titled, Do it Yourself Data Mining, it develops in great detail, the theoretical issues and the methodologies related to this method. Preparing the Text To prepare the text of each articles to be rated, first separate each sentence (defined by a period, question mark, or explanation mark), separated with by a paragraph. This is quickly accomplished using the Global Replace function in your word processor. Following conversion of this format to a table, each sentence is rated, as shown in the example below, (The “Comment” column is optional).

Sentence Types Each sentence is classified as being in one of the following categories: CLASS LEGEND

DESCRIPTION

ANALOG

Analogous scenario used to illustrate author’s view

AXIOM

Rhetorical or Self Evident statement. Such Statements seldom elicit disagreement, and are used as the foundation upon which premises are constructed.

Most importantly, they convey no information. CRITIC

A statement questioning validity of fact and/ or conclusion or logic of an assertion

EXPLAIN

Explanation, Cause/Effect, or interpretation of a statement. Frequently contains words like, “because,” “since,” or other cause-effect linkages.

FCON

Fact-based and/or logically derived conclusion

PCON

Personal Conclusion, representing a personal opinion, a value judgment, and/or emotionally based statement

PRED

Prediction of some future event or consequence

PRES

Prescription. Cues come from words like “should,” “must,” “ought to,” etc.

QUOTE

A quote from someone other than the writer

RQUES

Rhetorical Question

SQUES

Slanted Question

ERROR

Cannot be classified – either incomprehensible, logically flawed, has unknown purpose, or is completely off-topic

VFACT

Verifiable Fact

Ratings These numeric ratings are used to answer the question, “To what degree is this statement helpful to persuading the reader to agree with the author’s intended message?” These ratings are as shown here: Rating

Contribution to Goal

1

Very counterproductive to achieving writer’s goal

2

Somewhat counterproductive to achieving writer’s goal

3

Neutral

4

Somewhat useful in achieving author’s goal

5

Very useful in achieving author’s goal

The Two Articles Evaluated In order to process the results for multiple articles, they are combined together, with the author’s initials displayed in the first column. This rather extended set of ratings may be viewed by clicking this link:

The Results The table below illustrates the percentage frequency of each of the categories listed. Immediately evident is the structural difference in the approach the two authors use to present their views. JCG ANALOG CRITIC PCON EXPLAIN FCON PRED PRES QUOTE RHET RQUES SQUES ERROR VFACT Total

KG 7 0 37 6 28 8 1 0 1 0 3 0 7 100

total 0 6 32 12 6 0 0 5 3 4 9 15 8 100

3 3 35 9 17 4 0 2 2 2 6 8 8 100

Each of the values shown represents the percentage of each category, within its respective column. One thing which stands out is the difference in the use of Factual Conclusions (FCON), between JCG and KG. While both employ similar levels of Personal Conclusion (PCON) we shall see how these have differing impact on reaching their goals. There was an unexpectedly high number of statements scored as ERROR, in the KG article, marked as such because they were either indecipherable, factually in error, or were seriously off-point. The question which of whether the difference in structural approach, apparent in the table above could be reliably expected to occur in additional work by the two authors. Here, we turn to a test of Statistical Significance. The “p < .0001,” indicates that there is only a 1 in 10,000 chance a similar rating of two different articles by the same authors would NOT produce these same differences. Chisquare = p< 0.0001

65.8

with

12 df

It should be noted that KG had the far more challenging task. since even if successfully accomplished, rebuttal still requires an alternative solution, which while implied, was never overtly stated. Even more important than the structural analysis is the Effectiveness Rating given to each sentence or question.

Shown below, we see that the Effectiveness Rating most frequently appearing is either a “1,” Very ineffective, or the “4,” Moderately Effective.

When we break these by author, we see some major differences in the ratings achieved by each: Here are the number of ratings obtained by JCG:

Compare this with that of KG:

Here, with the sixteen ERROR ratings, which are always scored as a “1,” along with a number of the PCON statements, he loses the opportunity to make a convincing argument.

It should be noted that KG had the far more challenging task, since even if successfully accomplished, rebuttal still requires an alternative solution, which while implied, was never overtly stated. Finally, we can look at the Total Effectiveness which is derived by taking the sum of the ratings and dividing by five x total statements, Thus the effectiveness for JCG is Calculated as: 392/(5*95) = 83% and for KG, 221/(5*103) = 43% This translates into the likelihood, for a reader receptive to persuasion, that the respective articles will achieve that change in attitude. The Power of the PCON Ultimately, persuasion rests with the manipulation of the reader’s emotions. The statements which have been classified as PCONs are those which most directly reinforce or attack the presumed existing attitudes of the reader. Low ratings were attached to those statements which were seen as attacking the reader’s view, while the converse was true for the high ratings. To make these differences meaningful, her is an example of two JCG written PCON statements which were positively rated: “Barack Obama, while possessing that common trait that lives in all of us is not evil. “At least no more so than the rest of us.” These statements will anger no one, although some may disagree with their content. JCG PCON Ratings

Contrast this to these two inflammatory PCONs made by KG, when responding to JCG’s rhetorical statement that “… people have been choosing the lesser evil since the very dawn of man.” “This comment is grossly ignorant of history.” “People have not been choosing the lesser evil since the very dawn of man.” Along with the gratuitous rudeness and lack of respect contained in the first statement, that this author would chooses to debate a rhetorical comment, suggests a lack of understanding of the substantive issue under consideration – the merits of choosing an alternative candidate to Obama.

KG PCON Ratings

Where’s the Beef? While carefully crafted PCONs can establish receptiveness to a previously unacceptable position, there still remains the requirement that new actions, problem resolutions or actions are presented for adoption. Like a political party, an Op Ed article needs a “platform,” to advocate. As the table below suggests, the two authors differ widely on this Information/Emotion Continuum. They are nearly mirror images of each other in the weight of the categories of sentences which carry information vs. those which are emotional in content. Information ANALOG AXIOM CRITIC EXPLAIN FCON PRED QUOTE VFACT Total Info

JCG% 7.4 11.6 0.0 15.8 13.7 10.5 0.0 2.1 61.1

KG% 0.0 3.7 0.9 11.1 4.6 0.9 4.6 9.3 35.2

Emotion ERROR PCON PRES RQUES SQUES Total Emotional

JCG% 0.0 34.7 1.1 0.0 3.2

KG% 19.4 31.5 0.9 3.7 9.3

38.9

64.8

What we see in the KG article is an attack with neither a solution, nor even an attempt at delineating the consequences of whatever it is he wants the reader to do, or not do. He makes one limited attempt when he restates a JCG prediction: “This foundation of his argument makes predictions that McCain would ruin the Supreme Court, unions, science, ignore global warming, forget the poor and unemployed, maintain the gap between rich and poor, poorly address taxes, trash social security, and more baseless …” Yet, the JCG article, while in every way, evaluated as being far more persuasive to the position taken by its author, is the subject of frequent attacks by its readers. In an attempt to rate the response received by both authors, all the comments received (minus the author’s own responses) were classified as “Agree,” “Disagree,” or “Unknown.” The Unknowns were simply that. It was uncertain what, if any point, the commenter was making. This was the result obtained: Agree Disagree Unknown

JCG 35.7% 38.1% 26.2%

KG 11.1% 55.6% 33.3%

Total 31.4% 41.2% 27.5%

There is little that can be inferred from this information, except for the evident dominance of emotion as opposed to thoughtful formulation of ideas by those who have commented on both articles. Is it Worth the Work? If you have reviewed the extended display of the ratings for the combined articles, your first response is likely to be, “This is just gong to take too much work, and too much time.” It’s true that the combined articles contain 203 sentences, and that that configuring the tabular format takes a few minutes. For those lacking formal statistical tools, the longest part is calculating the many values in your existing spreadsheet program. For those interested in trying out this process, please contact me for assistance in preparing spreadsheet templates which can largely automate these quantitative findings. For those participating in this rating process, perhaps the greatest advantage is that the rater is forced to remove all emotion as a result of the intensive consideration given to each and every statement. If the author is rating his own work, this is an astonishingly effective tool which facilitates quality self-editing, as the author is forced to step back and look at the writing as would an outsider.