Advantage

This post originally appeared in Inside Higher Ed on 11/15/16

Current events have highlighted systemic racism in America yet again, and social media feeds continue to be inundated with posts about racism and police brutality. Often, these online conversations enter the classroom, lecture hall or other communal spaces within the university. This can often leave administrators, faculty members and students to fend for themselves during conversations that are, by their very nature, heated and laden with emotional content.

To address this, many people have turned to the language of privilege to structure conversations and unpack racism for those who may be predisposed to deny its very existence. Regardless of how popular the term “privilege” has become, I have never found it particularly useful in discussions, because it is too generic and abstract.

In fact, I believe that “privilege” is a sterile word that does not grapple with the core of the problem. If you are white, you do not have “white” privilege. If you are male, you do not have “male” privilege. If you are straight, you do not have “straight” privilege. What you have is advantage. The language of advantage, I propose, is a much cleaner and more precise way to frame discussions about racism (or sexism, or most systems of oppression).

Any and all advantages one can have are based — in part, or in whole — on a system of oppression designed to elevate certain innocuous expressions of humanity over others (skin color, sexual preference and so on). Thus, the language of advantage begins by first enumerating one’s advantages and understanding their origins.

For example, I am advantaged as a male. That advantage affords me a higher salary on average when compared to women, regardless of talent, which in turn affords the further advantage of enabling me to build wealth. If I were white, my advantages would grow. In the academy, I am also, perplexingly, better equipped to take advantage of paternity leave. Being male also enables me to express my opinions as though they were fact — my opinions in certain spaces are generally not questioned, or if they are, it is not assumed that I am wrong.

Those are simple examples, but they illustrate the point. Advantages can be summed up in a way that can generate a net advantage or disadvantage in certain spaces. This exercise is similar to a “privilege walk.” But it is different in that any advantages will not just net me a meaningless step forward in comparison to my peers. Thinking in this way forces me to understand what my advantages can, in fact, buy.

The distinction between “privilege” and “advantage” is important because “privilege” is not a particularly useful phrase to incite change in the minds or actions of others. No one wants to give up privileges. The entire idea of a privilege is based on possessing a special status that is somehow deserved. Privileges feel good.

Think about all of your privileges. Do you want to give them up? Does giving them up make you feel like you have somehow done someone a favor? (“Here you go … make sure you use this well.”) Or does giving up a “privilege” seem incoherent? It might, because generally privileges are given and taken by someone else. They are earned, and are seldom bad things to have.

Now try shifting your language to that of advantages. Ask yourself, “What advantages do I have over that person over there?” That question is much easier to answer and yields more nuanced responses. If I answer for myself, I can readily see that not all advantages are inherently problematic on their face. As a tall person I am advantaged in some spaces (e.g., reaching up to grab something from the high shelf in a supermarket), and disadvantaged in others (e.g., sitting in a cramped seat on an airplane). Yet if one looks under the surface, one can see that in both circumstances my (dis)advantage is predicated on design choices that are outside of my control. They are systemic. (It is also silly to say that I am tall privileged.)

What about a wealthy high school student who scored well on their SAT? They could unpack their success by understanding their advantage, for example: “Yes, my SAT scores are higher than someone else’s, but that may be because I have advantages in schooling that are predicated on the wealth of my community and/or parents. My schools are better, and I had access to tutoring. Moreover, some of that wealth is a result of oppressing people of color by historically denying them the ability to buy property in nicer areas, thus limiting their capacity to build and transmit wealth to their children. Those advantages are unearned, yet I still benefit from them. So, no, I won’t get bent out of shape if someone else with lower SAT scores is admitted into this fancy college and I’m not.”

The above example is more complex than my innocuous example about my height, but both have the same structure. They both require situating an advantage in a larger sociocultural context. While this is possible by using privilege, doing so can get clunky very quickly, and can shut down conversations before they become meaningful.

Unpacking systematically unfair systems through the language of advantage affords nuance. The poor white farmer lacks economic advantage but still possesses white advantage, and he can thus interact with law enforcement without fear. The wealthy black businessperson lacks racial advantage but can mitigate some of the negative effects of that through the strategic use of wealth. The difference? The white farmer will always be white. The black businessperson may not have always been wealthy, may lose his or her wealth, and his or her wealth can be ignored by a more powerful government.

The language of advantage also implies intersectionality, and this allows for a better understanding of one’s net advantage. For example, I am a Mexican-American man. I do not have “male privilege.” I am a man, and that affords certain unjust advantages when it comes to the salary I can earn and where I can work. However, for a person of color that salary may come with expectations for more service that, for all their merit, can be distracting and lead to less productivity.

All this leads to a certain uncomfortable truth: we are not — and have never been — equal when it comes to the advantages we possess. All lives do not matter equally in practice (although they should). It is time we adopt language around racism, sexism, etc., that helps move the conversation forward. Only then can we begin to measure and understand the mechanisms of inequality that lead to needless suffering.

When we shift the language to that of advantages and disadvantages, it foregrounds how unjust and arbitrary some of those advantages are — while also allowing us to quantify relative (dis)advantage better. The language of privilege, on the other hand, obfuscates the systems of oppression it is meant to highlight. It is time we move on from using it.

Thoughts on the “Imposter Syndrome”

Note: This post was originally published by Inside Higher Ed April 13th, 2015 under the tittle “We Are Not Imposters” 

To my fellow graduate students I say this: I am not an impostor, and neither are you.

You might not believe me, but before you begin pushing back against my assertion and dismantling your confidence with thoughts such as, “but peer X is a much better writer/researcher/scholar than I am, and I don’t belong here,” you should read on.

First the bad news: you might be right. Peer X might very well be more accomplished in the dimensions that you have identified. This is the reality in every human endeavor — and is especiallysalient in academe — but that doesn’t make you an impostor. Impostors are pretenders focused on maintaining an illusion of belonging in the spaces they invalidly inhabit. That isn’t you.

It really isn’t.

As Ph.D. students, we already belong. Those whom we admire and hope to emulate have invested many hours vetting us. We were admitted on the merit of both our accomplishments and how we communicated them. In many instances faculty have committed their time, energy and resources to mentoring us as we progress through our programs.

Their initial judgment is over, and we’ve won; our prize is being invited to join a community of scholars in our respective disciplines and contribute to them, albeit slowly and clumsily at first.

It’s O.K. to be slow and clumsy at first.

As budding academics we are tasked with understanding the work and methods that predate us so that we can begin to push the frontiers of our respective disciplines. This can be clumsy work, and it takes a certain amount of courage, resolve and optimism to quickly apply what we learn and to then have our work products reviewed by our more experienced peers (e.g., faculty and advanced students). This process can be uncomfortable, but we have to learn the ropes before we can meaningfully contribute to our community.

This work, moreover, is never finished. By definition we have chosen to enter into a community that is defined by its insatiable thirst for not only the next answer, but also the next question. So when things get tough, remember that there is never a “complete” academic. Everyone — even the most established faculty members — has questions they don’t (yet) have the tools to answer. That’s what makes the work exciting!

Anxiety comes with the territory.

Unfortunately the process of earning a Ph.D. also comes with mistakes, setbacks and dead ends. While anxiety is a natural consequence of these downsides, it would be a mistake to be overwhelmed by it. (When anxiety and depression have medical causes, we should seek help and also remember that, even in these cases, we are in good company.)

Let me be clear: negative feelings and feelings that we are not quite good enough quite fast enough are valid. As graduate students we are inundated with messages of others’ accomplishments. We’re even encouraged to write down all of ours in a C.V., making comparisons that much easier. What we feel as a consequence of these comparisons is natural. To move from feeling anxious to believing one is an impostor, however, is to invite disaster.

We are Novice Experts.

I argue that we are not impostors, we are simply “novice experts” trying to figure out how best to contribute. I have read and witnessed a pervasive tendency among my fellow graduate students, despite their personal histories of achievement, toward self-reproach and self-doubt when they encounter setbacks. While I think in small doses this habit helps us hone our work, too much of it is crippling. It’s O.K. to doubt a result; it’s not O.K. to doubt your validity as a Ph.D. student.

We are not the best at what we do; we are not all-star researchers; we are not tenured faculty members. We are none of these things because we are being trained to be some or all of them in the (near) future.

Importantly, we are not impostors — we are novice members of an academy that treasures expertise and accomplishments. In this role we will make mistakes not due to incompetence, but instead due to our inexperience. And as annoying as it may be, we have to muddle our way through inexperience before we can become experts.

Tips to avoid feeling like an impostor.

Throughout my time as a Ph.D. student I have found the following strategies helpful whenever I’m in danger of feeling like an impostor. This list is not exhaustive and may not work for you, but the spirit of the list is to remember that our place is one of dynamic growth, not of static accomplishment.

1. Build multiple relationships within your community.

One way to feel like a genuine member of a community (and not an impostor) is to actually get to know a lot of members in that community in an informal setting. Asking faculty or fellow students to lunch or coffee, for example, is a great way to nurture relationships that began in the classroom or other formal settings. This isn’t to say, however, that you should avoid scheduled meetings in an office. I’ve just found that these meetings can reinforce a presumed hierarchy (e.g., student/teacher) in a way that might inadvertently also fuel feelings of being an impostor. In general, I avoid formal meetings unless I want to have a formal conversation, usually about work.

2. Avoid idolizing your mentors and advisers.

Even the most established researchers still have their limitations. You might really like working with a prominent faculty member, but that person may not have time for regular meetings, or may tend to give superficial feedback. Perhaps they are not familiar with a methodology that you find important. Knowing and being at peace with these limitations this helps you set reasonable expectations for the relationship. It also helps you better understand and contextualize your own limitations.

3. Go to conferences — both your field’s flagship conference and a few niche ones.

Conferences are great places to learn the hidden curriculum of a field by observing how established members of the field interact with the world outside of their institutions. They’re also great places to explore professional identities. Going to a large flagship conference helps with picking up the larger norms and trends of the field, while smaller niche conferences are great for exploring new directions.

4. Celebrate your accomplishments, but avoid the dreaded humble brag.

I’ll admit it. Humble bragging is a pet peeve of mine. Here’s a great definition of it from Think Progress: A humble brag is when someone tries to hide self-promotion under a guise of modesty.

An example in academe might be worded, “I can’t believe I just got my paper accepted in [flagship journal]! My paper wasn’t even that good!”

Aside from being disingenuous (which is irritating in its own right), humble bragging might have an alienating effect and makes it particularly hard to congratulate you. Did you win that great fellowship or just get published? By all means celebrate and thank folks, but don’t hide behind false modesty. It doesn’t fool anyone, and in the age of social media everything we share has the potential for unintended consequences. (Perhaps the humble brag just got you uninvited from a dinner another student was organizing at the flagship conference you should be going to.)

5. Don’t fear peer review — embrace it.

Every piece of writing can be improved, and one way to understand the peer review process is to treat it like a form of collaborative writing. Reviewers comment on what jumps out at them, not on you as a person. Paying attention to the content of their feedback and not the form of it (maybe they weren’t kind) helps stave off feelings of being an impostor while also improving the final product.

It’s important to keep in mind that we are all members of academic communities with their own norms and standards, so rather than focus on the negative aspects of feedback, it’s generally more helpful to treat any piece of feedback as what is it: data that help improve one’s writing and calibrate it so that the important parts of it conform to the norms of the community.

It’s important to remember that in the end none of us are impostors because we all belong to academic communities that welcomed us and are invested in our success.

Python sctipt for MAXQDA timestamps

My dissertation is focused on the sense-making practices of college-students when they examine representations of their achievement. In order to get a handle of this phenomenon, I opted for a multiple-method approach—so I’ve been collecting both quantitative data, as well as qualitative data. As of now I have a couple of dozen interviews under my belt, and have both the audio and transcripts of those interviews to work with.

To analyze the qualitative data I’m using MAXQDA. One of the main reasons I chose it is because of its ability to sync transcripts to the original audio. Here’s an example:

Screenshot 2015-03-31 16.34.05

The image above is a screenshot of what this looks like. I can read what speaker 1 (S1) said, and click on the little clock icon to hear the audio. To me this feature was a must-have because it enables me to contextualize the text quickly and easily.

There’s a catch, however. (There’s always a catch!)

For this to work the timestamps in the transcript text files have to follow the (standard?) hh:mm:ss-ms format, and have “#” on either side. So a comment made 6 minutes into an interview would read #00:06:00-0#. Much to my dismay when I opened my transcripts I noticed that the timestamps were simply in mm:ss, so the 6 minutes read 06:00.

Needless to say, I wasn’t about to edit each transcript by hand. Doing so could lead to errors, and it would be time consuming (even if I used find and replace). Instead I opted to write a script in python to do all of the replacements for me. It took a bit of doing, but I was able to get the script to work. A special shout out goes to Jeff Stern and Adam Levick who helped me figure the script out!

If this is something you’re interested in trying yourself, read on!

Prep: What you’ll need

  1. A Mac (sorry PCs, I’ sure it works in roughly the same way but this is a mac-centric blog post)
  2. TextWrangler (or another text editor)
  3. MAXQDA (or other qualitative software that can link text to audio via a specific timestamp format)
  4. Transcripts, saved as test (.txt) files
  5. Patience

Step 1: Tell python which files it will be working with

First things first. Open your text editor and add the following lines of code, which simply tell python which file to open (“infile”), and which new file to save (“outfile”):

infile = open('my_raw_text.txt')
outfile = open('(MAXQDA) my_raw_text.txt', 'w')

Note that I didn’t specify a file path. This only works because I saved the python script in the same place that I saved my transcripts. I found this to be the easiest solution, but if you like keeping your transcripts in one folder and python scripts in another, then make sure to specify the file path in the code above.

Step 2: Specify the rules python will use for find and replace

Next you’ll need to define what it is python will be doing. To do that you’ll need to know all of your replacement rules. In my case this meant 1) fixing the beginning of the timestamp (adding # and hh), and 2) fixing the end of the timestamp (adding ms and #). You’ll notice that I do this second step twice, once for each speaker. (In retrospect this may be redundant, but it worked so I’m not complaining!)

I chose to name the rules “replacements.” This tells python: when you see the word ‘replacements,’ it’s referring to this set of relationships. For example:

00: will become #00:00:
02: will become #00:01:
03: will become #00:03:

…etc


replacements = [('00:','#00:00:'), ('01:','#00:01:'), ('02:','#00:02:'),

				('1 S1','1-0# S1'), ('2 S1','2-0# S1'), ('3 S1','3-0# S1'),

				('1 S2','1-0# S2'), ('2 S2','2-0# S2'), ('3 S2','3-0# S2'),
				('0 S2','0-0# S2')]  

I just pasted a few of the rules. Scroll to the end to see the full script with all of them.

Step 3: Tell python what to do with the relationship you just defined

Here’s where the magic happens. The final step is to tell python what do actually do. Line 1 tells python where to look (your file). Line 2 tells python to look for t1 and t2 in replacements (which you defined in Step 2).  Line 3 defines what will happen (replace t1 with t2), and line 4 tells python to write everything in your new file outfile, which was defined in Step 1.

for line in infile:
    for t1, t2 in replacements:
   		line = line.replace(t1, t2)
    outfile.write(line)

Step 4: Tell python to close your source file, and your new file

Now that all the work has been done you simply tell python to close the two files it used.

infile.close()
outfile.close()

Step 5: Run the script!

You’re ready to go. If you’re using TextWrangler you can save the file with the extension .py so that TextWrangler knows that you’ve written a script. (I would recommend doing so early so that your syntax gets highlighted.) Once you’ve saved your file you can select run from the last drop down menu:

Screenshot 2015-03-31 17.50.20

 

Note that if you’re successful it’ll seem like nothing has happened. Rest assured something has! Just go to the folder where your python and transcript files are, and you should see a new text file with the name you used for “outfile.”

Summary: The full script

That’s it! Using python for simple—but tedious—find and replace tasks isn’t that painful after all. Here’s the entire script, including all of the find and replace relationships. Note that I only went up to 59 minutes. If your interviews are longer than that you’ll need to add the extra times into the replacements part of the code.


infile = open('my_raw_text.txt')
outfile = open('(MAXQDA) my_raw_text.txt', 'w')

replacements = [('00:','#00:00:'), ('01:','#00:01:'), ('02:','#00:02:'),
			    ('03:','#00:03:'), ('04:','#00:04:'), ('05:','#00:05:'), 
			    ('06:','#00:06:'), ('07:','#00:07:'), ('08:','#00:08:'),
			    ('09:','#00:09:'), ('10:','#00:10:'), ('11:','#00:11:'), 
			    ('12:','#00:12:'), ('13:','#00:13:'), ('14:','#00:14:'),
			    ('15:','#00:15:'), ('16:','#00:16:'), ('17:','#00:17:'),
			    ('18:','#00:18:'), ('19:','#00:19:'), ('20:','#00:20:'),
			    ('21:','#00:21:'), ('22:','#00:22:'), ('23:','#00:23:'), 
				('24:','#00:24:'), ('25:','#00:25:'), ('26:','#00:26:'),
				('27:','#00:27:'), ('28:','#00:28:'), ('29:','#00:29:'),
				('30:','#00:30:'), ('31:','#00:31:'), ('32:','#00:32:'),
				('33:','#00:33:'), ('34:','#00:34:'), ('35:','#00:35:'), 
				('36:','#00:36:'), ('37:','#00:37:'), ('38:','#00:38:'),
				('39:','#00:39:'), ('40:','#00:40:'), ('41:','#00:41:'),
				('42:','#00:42:'), ('43:','#00:43:'), ('44:','#00:44:'),
				('45:','#00:45:'), ('46:','#00:46:'), ('47:','#00:47:'), 
				('48:','#00:48:'), ('49:','#00:49:'), ('50:','#00:50:'),
				('51:','#00:51:'), ('52:','#00:52:'), ('53:','#00:53:'),
				('54:','#00:54:'), ('55:','#00:55:'), ('56:','#00:56:'),
				('57:','#00:57:'), ('58:','#00:58:'), ('59:','#00:59:'),
				
				('1 S1','1-0# S1'), ('2 S1','2-0# S1'), ('3 S1','3-0# S1'),
				('4 S1','4-0# S1'), ('5 S1','5-0# S1'), ('6 S1','6-0# S1'),
				('7 S1','7-0# S1'), ('8 S1','8-0# S1'), ('9 S1','9-0# S1'),
				('0 S1','0-0# S1'),

				('1 S2','1-0# S2'), ('2 S2','2-0# S2'), ('3 S2','3-0# S2'),
				('4 S2','4-0# S2'), ('5 S2','5-0# S2'), ('6 S2','6-0# S2'),
				('7 S2','7-0# S2'), ('8 S2','8-0# S2'),	('9 S2','9-0# S2'),
				('0 S2','0-0# S2')]  
			
for line in infile:
    for t1, t2 in replacements:
   		line = line.replace(t1, t2)
    outfile.write(line)       
infile.close()
outfile.close()

My Dissertation in One Picture

I’m fortunate to have been invited to attend the 5th annual Learning Analytics and Knowledge Conference (LAK ’15), where I will take part in the Doctoral Consortium (DocCon). I’m excited to get feedback on my dissertation design from the DocCon organizers and my fellow graduate students. Aside from a 15 minute presentation, we were also asked to design a poster for the poster session on Wednesday. I took the opportunity to design a visual “elevator pitch” in the form of the graphic below.

 

Screenshot 2015-03-15 14.49.13

 

The imperative that drives my dissertation design goes a little like this:

  • Students usually strive to do their best in their coursework.
  • Learning Analytics often take students’ achievement data (e.g, grades on exams, papers, etc.), and use the information to generate representations that communicate students’ progress and setbacks in their courses. The presumption: more information = good and generative of adaptive behaviors.
  • Students see these representations (on a dashboard, perhaps), and may adjust their strategies accordingly. However, before this happens they first have to “make sense” of the representation.
  • During this sense-making process, the representation may have motivational implications for students. For example, what happens if students see their average course performance displayed over time, as well as their classmates? Achievement Goal Theory suggests that such comparisons may, in fact, be motivationally maladaptive for students. As with most things it depends on the person as well as the context.

My dissertation, Exploring and Measuring Students’ Sense-Making Practices Around Representations of their Academic Information, is focused on better understanding this process. I utilize a multi-methods design that begins with interviewing students to explore how they make sense of various line graphs depicting different scenarios. These interviews will then help me draft and validate a measure that captures students “information motivation orientations” (working tittle). Once I finish my dissertation I hope to come away with both a validated measure, as well as design implications that can help inform the design of future learning analytics interventions.

Have feedback? Use the comment box below to share them, or contact me directly.