There are five propositions on tomorrow’s ballot. In researching them, Lena came across the Cal-Access Campaign Finance Activity: Propositions & Ballot Measures.

Unfortunately, for each proposition, you have to click through each committee to get the details for the amount of money they’ve raised and spent. Here’s a run-down in visual form, the only data manipulation I did was round to the nearest dollar. Note: no committees formed to support or oppose Proposition 13.

Here’s how much money was raised, by proposition:

Money Raised

Just in case you didn’t get the full picture, here is the same data plotted on a common scale:

Money Raised (common scale)

And the same two plots for money spent¹:

Money Spent

Money Spent (common scale)

It could just be my perception of things, but I get pretty suspicious when there’s a ton of money involved in politics, especially when it’s this lopsided.

The only thing I have to add is you should Vote “YES” on Prop 15, because Larry Lessig says so, and so do the Alameda County Greens!

Update #1: Let me write it out in text, so that the search engines have an easier time finding this. According to the official record from Cal-Access (Secretary of State), as of May 22nd, 2010, there were $54.4 million spent in support of various propositions, most notably $40.5 million on Prop 16, $8.9 million on Prop 17, and $4.6 million on Prop 14. Compare that with a “grand” total of less than $1.2 million spent to oppose them, with a trivial $78 thousand (!!) to oppose Prop 16′s $40.5 million deep pockets.

Update #2: The California Voter Foundation included more recent totals (they don’t seem to be that different), as well as a listing of the top 5 donors for each side of a proposition in their Online Voter Guide.

Also, here’s the python code used to generate these plots (enable javascript to get syntax highlighting):

# Create contributions and expenditures bar charts of committees supporting and
# opposing various propositions on the California Ballot for June 8th, 2010
# created by Paul Ivanov (http://pirsquared.org)

# figure(0) - Contributions by Proposition (as subplots)
# figure(1) - Expenditures by Proposition (as subplots)
# figure(2) - Contributions on a common scale
# figure(3) - Expenditures on a common scale

import numpy as np
from matplotlib import pyplot as plt
import locale

# This part was done by hand by collecting data from CalAccess:
# http://cal-access.sos.ca.gov/Campaign/Measures/
prop = np.array([
     4650694.66, 4623830.07    # Yes on 14 Contributions, Expenditures
    , 216050, 52796.71         # No  on 14 Contributions, Expenditures
    , 118807.45, 264136.30     # Yes on 15 Contributions, Expenditures
    , 200750.01, 86822.79      # No  on 15 Contributions, Expenditures
    , 40706258.17, 40582036.58 # Yes on 16 Contributions, Expenditures
    , 83187.29,	78063.91       # No  on 16 Contributions, Expenditures
    , 10328675.12, 8932786.06  # Yes on 17 Contributions, Expenditures
    , 1229783.79, 965218.48    # No  on 17 Contributions, Expenditures
    ])
prop.shape = -1,2,2 

def currency(x, pos):
    """The two args are the value and tick position"""
    if x==0:
        return "$0"
    if x < 1e3:
        return '$%f' % (x)
    elif x< 1e6:
        return '$%1.0fK' % (x*1e-3)
    return '$%1.0fM' % (x*1e-6)

from matplotlib.ticker import FuncFormatter
formatter = FuncFormatter(currency)

yes,no = range(2)
c = [(1.,.5,0),'blue']  # color for yes/no stance
a = [.6,.5]             # alpha for yes/no stance
t = ['Yes','No ']       # text  for yes/no stance

raised,spent = range(2)
title = ["Raised for", "Spent on" ] # reuse code by injecting title specifics
field = ['Contributions', 'Expenditures']

footer ="""
Data from CalAccess: http://cal-access.sos.ca.gov/Campaign/Measures/
'Total %s 1/1/2010-05/22/2010' field extracted for every committee
and summed by position ('Support' or 'Oppose').  No committees formed to
support or oppose Proposition 13. cc-by Paul Ivanov (http://pirsquared.org).
""" # will inject field[col] in all plots

color = np.array((.9,.9,.34))*.9 # spine/ticklabel color
plt.rcParams['savefig.dpi'] = 100

def fixup_subplot(ax,color):
    """ Tufte-fy the axis labels - use different color than data"""
    spines = ax.spines.values()
    # liberate the data! hide right and top spines
    [s.set_visible(False) for s in spines[:2]]
    ax.yaxis.tick_left() # don't tick on the right

    # there's gotta be a better way to set all of these colors, but I don't
    # know that way, I only know the hard way
    [s.set_color(color) for s in spines]
    [s.set_color(color) for s in ax.yaxis.get_ticklines()]
    [s.set_visible(False) for s in ax.xaxis.get_ticklines()]
    [(s.set_color(color),s.set_size(8)) for s in ax.xaxis.get_ticklabels()]
    [(s.set_color(color),s.set_size(8)) for s in ax.yaxis.get_ticklabels()]
    ax.yaxis.grid(which='major',linestyle='-',color=color,alpha=.3)

# for subplot spacing, I fiddle around using the f.subplot_tool(), then get
# this dict by doing something like:
#    f = plt.gcf()
#    adjust_dict= f.subplotpars.__dict__.copy()
#    del(adjust_dict['validate'])
#    f.subplots_adjust(**adjust_dict)

adjust_dict = {'bottom': 0.12129189716889031, 'hspace': 0.646815834767644,
 'left': 0.13732508948909858, 'right': 0.92971038073543777,
 'top': 0.91082616179001742, 'wspace': 0.084337349397590383}

for col in [raised, spent]: #column to plot - money spent or money raised
    # subplots for each proposition (Fig 0 and Fig 1)
    f = plt.figure(col); f.clf(); f.dpi=100;
    for i in range(len(prop)):
        ax = plt.subplot(len(prop),1, i+1)
        ax.clear()
        p = i+14    #prop number
        for stance in [yes,no]:
            plt.bar(stance, prop[i,stance,col], color=c[stance], linewidth=0,
                    align='center', width=.1, alpha=a[stance])
            lbl = locale.currency(round(prop[i,stance,col]), symbol=True, grouping=True)
            lbl = lbl[:-3] # drop the cents, since we've rounded
            ax.text(stance, prop[i,stance,col], lbl , ha='center', size=8)

        ax.set_xlim(-.3,1.3)
        ax.xaxis.set_ticks([0,1])
        ax.xaxis.set_ticklabels(["Yes on %d"%p, "No on %d"%p])

        # put a big (but faded) "Proposition X" in the center of this subplot
        common=dict(alpha=.1, color='k', ha='center', va='center', transform = ax.transAxes)
        ax.text(0.5, .9,"Proposition", size=8, weight=600, **common)
        ax.text(0.5, .50,"%d"%p, size=50, weight=300, **common)

        ax.yaxis.set_major_formatter(formatter) # plugin our currency labeler
        ax.yaxis.get_major_locator()._nbins=5 # put fewer tickmarks/labels

        fixup_subplot(ax,color)

    adjust_dict.update(left=0.13732508948909858,right=0.92971038073543777)
    f.subplots_adjust( **adjust_dict)

    # Figure title, subtitle
    extra_args = dict(family='serif', ha='center', va='top', transform=f.transFigure)
    f.text(.5,.99,"Money %s CA Propositions"%title[col], size=12, **extra_args)
    f.text(.5,.96,"June 8th, 2010 Primary", size=9, **extra_args)

    #footer
    extra_args.update(va='bottom', size=6,ma='left')
    f.text(.5,0.0,footer%field[col], **extra_args)

    f.set_figheight(6.); f.set_figwidth(3.6); f.canvas.draw()
    f.savefig('CA-Props-June8th2009-%s-Subplots.png'%field[col])

    # all props on one figure (Fig 2 and Fig 3)
    f = plt.figure(col+2); f.clf()
    adjust_dict.update(left= 0.06,right=.96)
    f.subplots_adjust( **adjust_dict)
    f.set_figheight(6.)
    f.set_figwidth(7.6)

    extra_args = dict(family='serif', ha='center', va='top', transform=f.transFigure)
    f.text(.5,.99,"Money %s CA Propositions"%title[col], size=12, **extra_args)
    f.text(.5,.96,"June 8th, 2010 Primary", size=9, **extra_args)

    extra_args.update(ha='left', va='bottom', size=6,ma='left')
    f.text(adjust_dict['left'],0.0,footer%field[col], **extra_args)

    ax = plt.subplot(111)
    for stance in [yes,no]:
        abscissa=np.arange(0+stance*.30,4,1)
        lbl = locale.currency(round(prop[:,stance,col].sum()),True,True)
        lbl = lbl[:-3] # drop the cents, since we've rounded
        lbl = t[stance]+" Total"+ lbl.rjust(12)
        plt.bar(abscissa,prop[:,stance,col], width=.1, color=c[stance],
                alpha=a[stance],align='center',linewidth=0, label=lbl)
        for i in range(len(prop)):
            lbl = locale.currency(round(prop[i,stance,col]), symbol=True, grouping=True)
            lbl = lbl[:-3] # drop the cents, since we've rounded
            ax.text(abscissa[i], prop[i,stance,col], lbl , ha='center',
                    size=8,rotation=00)

    ax.set_xlim(xmin=-.3)
    ax.xaxis.set_ticks(np.arange(.15,4,1))
    ax.xaxis.set_ticklabels(["Proposition %d"%(i+14) for i in range(4)])
    fixup_subplot(ax,color)

    # plt.legend(prop=dict(family='monospace',size=9)) # this makes legend tied
    # to the subplot, tie it to the figure, instead
    handles, labels = ax.get_legend_handles_labels()
    l = plt.figlegend(handles, labels,loc='lower right',prop=dict(family='monospace',size=9))
    l.get_frame().set_visible(False)
    ax.yaxis.set_major_formatter(formatter) # plugin our currency labeler
    f.canvas.draw()
    f.savefig('CA-Props-June8th2009-%s.png'%field[col])

plt.show()

1. I don’t fully understand what these numbers mean, as some groups’ “Total Expenditures” exceed their “Total Contributions” and still had positive “Ending Cash”

Arizona sucks!

It turns out that even documented immigrants agree, and I have the graphs to prove it!

You see, it all started when I took a great Visualization course this past term which was taught by Maneesh Agrawala. Maneesh gave enough structure for the assignments, but also left some aspect of each open ended. For example, our first assignment had a fixed dataset which everyone had to make a static visualization of, but the means by which we did that was entirely up to us. A lot of people used Excel (in graduate level CS class? gross!), some people wrote little programs (I wrote mine in python using matplotlib and numpy, and did some cool stuff that I will have to post about another time and contribute back to matplotlib), there was even a poor sap who did it all in Photoshop, as I recall, but anything was fair game. Turns out we could even just draw or make something by hand and turn it in!

The second assignment, the source of my graphs which quantitatively demonstrate the suckiness of Arizona, required us to use interactive visualization software to iteratively develop a visualization by first asking a question, then making a visualization to address this question, and going back several times refine the question and make successive visualizations.

On thing to keep in mind is that, overall, naturalized citizens are both an exclusive and a discerning lot. In most cases, you have to be a permanent resident (have a Green card) for 5 years before you can apply. And there are quotas for how many people can get a Green card every year, so there are lots of hoops to jump through. Given the
amount of effort involved, wouldn’t it be nice to look at a breakdown of naturalized citizens by state? Because that would give us an idea about which states immigrants percieve as, for lack of a better word, “awesome”, or if you’re
not so generous, “least sucky”. I bet you’ll feel that this second description is more appropriate once you take a look at the data, but keep my “least sucky” premise in mind as you read my original write-up which focused on a different angle (but from which we can still draw some reasonable conclusions). I’ll return to make a few more comments about the title of this post after the copy-pasted portion.

here’s my original write-up:

begin cut —>

There are three kinds of lies: lies, damned lies, and statistics.

As an immigrant, I’ve always had the subjective feeling that about half of the people I’m acquainted with are either themselves immigrants, or the children of immigrants. The US prides itself in being a melting pot, a country built by immigrants, so I wanted to dive into the data that would help me understand just how large of a role immigration plays in terms of the entire country. The question I started with, for the purpose of this assignment is this:

What’s the relationship between naturalizations and births in the US?

But what I really wanted was to find out was what kind of question do I need to ask to get the answer that would be consistent with my world view. :)

To do this, I started with the DHS 2008 Yearbook of Immigration Statistics, which was linked from the class website.

The file I started with was natzsuptable1d.xls, which required cleanup before I could read it into Tableau. Turns out that even though “importing” to tableau format is supposed to speed things up, it seems very fragile and would regularly fail when I tried converting type to Number (there were some non-numeric codes, like ‘D’ for ‘Data withheld to limit disclosure). **NOT** importing to Tableua’s desired format also had the added benefit of allowing me to change the .xls files externally, and having all the adjustments made in Tableau, without having to re-import the data source.

Frustratingly, the last column and last row kept not getting loaded in Tableau! I also ran into an issue which I think had to do with the ‘Unknown’ country of origin and ‘Unknown’ state of naturalization which made the totals funky. It took a while to figure out, but there was a problem with Korea, because there was a superscript 1 by it, indicating that data from North and South Korea were combined.

I was trying to use the freshest data possible, so I used the CDC’s National Vital Statistics System report titled Births: Preliminary Data for 2007. I just had to copy paste the desired data, and massage it to fit the proper order columns in the excel table I already had handy. I put zeros for U.S. Armed Services Posts and similar territories which is probably not accurate, but this data was not available in the reports that I found. Interesting factoid: according to NVSS (CDC), in 2007 there were more people born in NYC than the rest of the state combined. (about 129K vs 126.5K). The only caveat with this data is that it contains only 98.7% of the data. The states with some missing portion of their data tabulated are Michigan (at 80.2% completeness), Georgia (86.4%), Louisiana (91.4%), Texas (99.4%), Alaska (99.7%), Nevada (99.7%), Delaware (99.9%). Thus, state-level analysis for MI, GA, and LA may be distorted.

The data I had from DHS is for Fiscal Year 2008, which, as it turns out, goes from October 1st, 2007 – Sept 30th, 2008. Thus, no matter which combination of NVSS and DHS datasets I used, there would necessarily be a mismatch in the date range covered by each, so I settled with describing my visualization as “using the latest available data”, noting the actual dates for each dataset in the captions. Also, the NVSS report contained a graph of births over time, which fluctuates very modestly from year-to-year, thus the visualization would not change qualitatively if I had 2008 birth data on hand.

I was having a really hard time trying to get a look at the data I wanted to see in one sheet, and ended up trying to make a dashboard that combined several sheets. I couldn’t figure out a good way to link the different states across datasets. I struggled for quite a while to pull out the data that I wanted to look at, and ended up having to copy past everything from DHS and NVSS (transposed) onto a new sheet in Gnumeric.

Here’s the result:

Initial visualization

So, in all of the US, about 1 in 5 new american citizens is an immigrant, or for every four births, we have one naturalization. That was kind of unsatisfying. I’ve lived in California the entire time I’ve been in the US, and I feel that at least California is more diverse than that. There’s all those states in the middle of the country that few people from the rest of the world would want to immigrate to, yet the people living in them are still having babies, throwing off the numbers which would otherwise support my subjective world view…

So I decided to look at the breakdown by state.

Broken down by state, what’s the relationship between naturalizations and births in the US?

my second iteration

I added the reference lines so that you could both read off the approximate total easier, and be able to do proportion calculations visually, instead of mentally. This started looking promising, as I’ve only lived in California, and it looks like it’s got quite a lot of immigrants as a portion of total new citizens.

It was still kind of hard to see the totals, so I decide to create my very first calculated field – which would had the very simple formula [Births in 2007]+[Total Naturalized]. Using this new field, I could now make a map, to see the growth broken down geographically. This was just a way of reaffirming my earlier bias against the middle states having babies without attracting a sufficient number of immigrants to conform to my world view.

gratuitous map (was too easy to do using the software)

In the breakdown by state bar graph, it was also difficult to visually compare the total births by state, because they all started at a different place, depending on the number of naturalizations for that state. So I decided to split the single bar and make small multiples for each state.

back to something more interpretable

It’s interesting that the contribution of naturalizations slightly changes the ordering of the growth of states. For example, Florida has fewer births than New York, yet it’s total growth is larger, because it naturalized 30,000 more people than New York. With this small multiples arrangement, it was now possible to do positional comparisons across categories, not just between naturalizations and totals. Turns out that more people get naturalized in California than are born in the entire state of New York. And since New York has the third highest number of births annually, more people got naturalized in California than are born in any state other than CA and TX.

This was too large of a graph, and the story I’m interested in is really the ratio between the birth and naturalizations (the closer to 1:1, the better), so I made another calculated field, which is exactly such a ratio, multiplied by a factor of a thousand, so I could give it a sensible description (Naturalizations per 1000 births). This refines my question

For every 1000 people born in the US, how many many immigrants become naturalized?

I then ordered on these ratios, and decided to filter the top states. Guam would have made the cut, but it is not a state, and (though I didn’t mention it earlier) it’s NVSS birth data was only 77% complete, so I excluded it. Fifteen is a nice odd number, but it actually marked a nice transition, as after Texas, everything else is less than 200 naturalizations per 1,000 births.

The small multiples bar graphs still looked too busy, and there was redundancy in the data, which didn’t tell a succinct story. So I switched to just look at the ratios alone. This revealed, that, indeed, the fact that I’ve been living in California makes my perspective quite unique, as it is one of three states, along with Florida and New Jersey, to have an outstandingly large number of naturalizations compared to births. It is so high, indeed, that it puts the naturalization per births rate in these three states at more than twice the national average!

Looking at ratio alone tells us about the diversity in each states growth, but carries more meaning in the context of total growth . Thus, added the combined totals (naturalizations and births) as a size variable, for context. The alternating bands to both make it easier to read off the rows, and to aid the comparison of sizes by framing every data point in a common reference window. It obviates that California is the state with 864,261 new citizens because fills the frame completely.

Final question: What are the Top 15 “Melting Pot” States?

almost done, would be nice to include context from the visualization I started with

Ordering the data in this way also shed light on the small but still very diverse states that would not have otherwise made the cut (and did not pop out in any manner on my previous bar graphs). Rhode Island and Hawaii got it going on, in terms of attracting immigrants.

Certainly the fact that I’m an immigrant myself also greatly influences whom I associate with, further skewing my world view towards a 1:1 ratio, but I’m actually quite impressed with just how close to that ratio is in California – 1:1.9. Of course, the data I’ve analyzed does not include the American-born 1st generation of children, nor does it take into account the number of immigrants living in the US that do not have citizenship. All of these factors would surely push the ratio even closer toward 1:1.

I decided to combine the US total growth information, since it’s gives further perspective on the entire data set, such as the fact that California accounts for about 16% of total US growth. It also sheds light on how the US average was calculated. A new “twice the nat’l avg” line makes explicit the three most diverse outlier states mentioned before. I also changed the colors to match the convention used in the bar charts made earlier. The US combined total line semantically links the data plotted with the national growth bar chart – i.e. the green dots are formed by the sum of born and naturalized citizens.

What are the Top 15 "Melting Pot" States?

<—- end of cut

Ok, so, to be honest, it turns out that I wrote a large chunk of this post (Arizona suckage included) before I actually looked back at my visualizations, only going off my memory that it wasn’t in the top 10. So Arizona is just below the national average in this “Melting Pot” ratio (a measure I made up, the number of naturalization per 1000 births). Since it is #12, some might say, “Paul, Arizona’s on your top 15 list”, to which I’ll reply: “So’s Texas.”

I guess I just wanted to share these purdy graphs I made a few months back, and it seemed like there was a somewhat topical angle on them a few weeks back, when I remembered that I hadn’t posted them on here yet. Anyway, I’d love to hear back your thoughts.

One of the excuses for the tour is the paperback release of Standing up to the Madness: Ordinary Heroes in Extraordinary Times by Amy and David Goodman.

Now that I’m a tenured grad student, I can actually allow myself to read for pleasure – guilt free! So I went to the library that Thursday, and picked up the hardcover, which came out last year.

What I liked about this book is what sets it apart from other political books of today. Amy and David don’t just provide us with a laundry list of wrongdoing by the Bush administration, congress, various governmental agencies, as well as highlighting some of the ongoing local struggles. Though the book is chock-full of such details, they are all provided in the context of a particular vignette. What’s more – instead of simply stating the problems, or providing an outline of the authors’ opinions regarding what course of action should be taken, the book highlights the work average citizens have already done to oppose injustice, censorship, racism, etc. One example is T-shirt “terrorist” Raed Jarrar, who wore a shirt with the words “We will not be silent” – written in both English and Arabic – a reference to the White Rose – and was forced to put another shirt over it because JetBlue customers were threatened or offended. With the help of the ACLU, Jarrar sued the TSA and JetBlue, who ended up paying $240,000 to settle the discrimination charges.

Like Hochschild’s King Leopold’s Ghost¹, this book is non-fiction that reads like fiction. Not because it is well-written, though it is, but because of the shocking realities of the content. Leadership cannot be taught, it can only be revealed. Standing up to the Madness gives us dozens of snapshots of the ongoing work of ordinary heroes.

1. which, after I first read it in 2001 became my measuring stick for gauging the quality of non-fiction

I find it very difficult to engage in debates about national politics because the average citizen has so little influence over these matters. I think that it’s much more worthwhile to get informed about and involved in local politics, because that’s where someone like me can actually have influence.

Nevertheless my own answer to the question is that it’s probably not a good thing. There’s this high-dimensional landscape of issues that people care and have different ideas about – reproductive rights, gun control, immigration, education, social programs, the size of government, taxation, the list goes on and on. Yet that gets projected down to this one dimensional line with just “Left” and “Right” with optional “far” and “center” prefixes.

And, sadly, the common consensus is that on election day you have only two possible boxes to check. A single decision. One bit. 0 or 1.

The Democrats and Republicans are playing a small concessions type of game. They sort of shuffle around slightly to appeal to enough of those voters who aren’t already automatically voting for them. If you only vote for one or the other, they have no reason to change – they already have your vote.

Voters in safe rarely contested states, have the unique opportunity to vote their conscience without fear¹. When I twittered about Obama’s support for the FISA Compromise, Philip, a disappointed California voter replied: “our voting system forces us to vote strategically and i’ll be voting obama .” This doesn’t make any sense to me! Obama will carry California. Democrats almost automatically get California² .

So why give in? You’re not happy with the Democratic candidate³, the candidate who will carry California regardless of how you vote, yet you still feel unable to voice your disapproval in the electoral arena. David wrote: “I’m not going to throw away my vote on the green party,” but aren’t you just throwing away your vote to the democrats, instead?

The role of third parties is to emphasize new and different ideas, to bring folks who’ve given up hope back to the table, and to make the major parties shift in MEANINGFUL ways. Here are some great YouTube clips on the role of third parties in the US: Part One, Part Two, Part Three, Part Four, Part Five.

If you still have doubts about voting for a third party candidate and/or you live in a swing state – consider the votepact.org proposal: find a fellow kindred heart on the other side of the political spectrum who’s also unhappy with the candidate on their side, and together vote for a third party (fill out your absentees together over coffee).

1. Electoral College: bug or feature?
2. The only way the Democrats might not get California is if Arnold runs as VP for a moderate Republican, and that just is not happening this year.
3. There are more reasons to not be happy

23:37 May 10th, 2007 Thursday

So what good is all of this if we don’t engage one another – on a very real level? “Only a Sith Lord deals in absolutes,” man – so let’s get off the condescending trips and the polarizing anti-discourse. Let’s use our full range – zero, one, two, five, seven, eight, ten, etc and not just on or off – there’s an infinitude of wonder in between and out in every dimension.

I resolve to hesitantly dip my toes in, from time to time, instead of being all in or all out. Talk to a beat stranger – but not every beat stranger. Give up some left over food to the guys on Bancroft and Telegraph (the Shattuck hobos are too hip for me – but I hope they aren’t for someone else — I know they aren’t). I can just eavesdrop on the world from time to time – I can’t always be wide-eyed gulping from the fire hydrant of information flowing at 100 terabits per second eyes glazed over passed out exhausted gasping for a sense of self disoriented head-spun hours or days later. Just a drinking fountain or a tap and a few cups or liters a day would be fine. No need to parch yourself and dry up like a raisin all the time. It’s ok to wrinkle and shrivel – and you don’t need to burst, either – just be uncomfortably sincere.

I think I’m going to try that.

Related brief thought:

17:36 June 22nd, 2007 Friday

Bumper Sticker Activists (Telegraph in Berkeley)
The last thing we need is more Bumper Sticker Activism. Wearing a clever T-shirt does not constitute civic participation.

last year and this year.

After watching the videos, you can play with Gapminder yourself as it is a web-based tool.

More info and tool links at gapminder.org.

I’ve started getting involved with the (currently small) Campus Greens organization (which meets Mondays at 7:10 in 200 Wheeler).

So today I heard Todd Chretien, Green senatorial candidate speak to a group of about 30 as part of the ASUC Speaker Series. Todd titled his talk “Why Students Should Never, Ever Vote for the Democrats,” which I think is somewhat unfortunate. Todd has an eloquent platform and I share a lot of the same views, but I also think that the title incites the type of reaction that eliminates any possibility for reasonable discussion or discourse.

I think that people don’t want to listen to you if you insult them, or just say something shocking – the novelty (if any) quickly wears off (it’s taken me a while to figure this out, but I think I learned the difficulty in trying to actively engage those who support the Democrats when talking (ranting?) to Janet on the streets of Brussels over the summer).

I think that we need more boring nitty-gritty politics, because no one will hand over the helm to people with big ideas (even if they are the right ideas). The big picture is important, but it has to be negotiated with real, tangible, local progress.

Todd gave a short run through of his top three issues ( war in Iraq, education, the two party system), and then opened it up for Q & A. In answering the questions, he covered a lot of ground in both domestic and foreign policy, but I felt like it was a discussion of issues larger than those someone who admitted he had no chance of winning could hope to influence….

So as the last question for the night, after expressing these sentiments I asked what we could do locally, that’s within our power, mentioning current choice voting efforts in Davis and Oakland. Unfortunately, Todd stuck to his anti-war protest-in-the-streets approach (even taking an outlandish pot shot at proportional representation by mentioning something about Hitler getting elected).

Most of my life I, too, have been a big ideas person, but I can’t say I’ve accomplished much with them, which is why I’m trying something new…

Until I came to UC Davis, I had never realized that there *could* be different voting systems. Choice voting is a way of reaching a majority (greater than 50%) consensus.

Choice voting allows everyone to vote their conscience without the fear of having your vote “wasted.” After the polls close, if your top-ranked candidate, Alice, has the least amount of votes, she is eliminated and your vote transfers to your next choice, Bob, in your order of preference. This process (“instant run-off”) continues until candidates reach enough votes to be elected (the threshold). This consensus building mechanism ensures that the elected officials will represent the greatest possible proportion of the voters.

Contrast this with the current system: candidate Mallory and Minnie, representing a minority of the population could get elected when multiple similar candidates (Alice, Bob, Chris, and Debra) representing the viewpoints of the majority of the population split the vote between one other.

This would not happen under choice voting, because when Alice is eliminated, those votes would go to the next choices of her supporters. This would provide more votes for the remaining majority candidates, ensuring that one of them gets elected.

I encourage Davis voters to vote yes on Measure L this November so that the City can continue looking into this effective system.

Paul Ivanov
UC Davis Class of 2005

(cute choice voting promotional video)