12/6-12/13: Registration Open for VBI 2024, LD & PF Tournament Results, and How to Master a Topicality 2NR
Registration for VBI 2024 is Open!
We are pleased to announce our initial 2024 dates and locations:
· VBI Philadelphia - June 29-July 12 at Swarthmore College (Public Forum and Lincoln Douglas)
· VBI San Diego - July 14-27 at the University of San Diego (Public Forum, Lincoln Douglas, and World Schools Debate)
· VBI Los Angeles - July 28-August 10 at UCLA (Public Forum and Lincoln Douglas)
Stay tuned for more information as we roll out announcements over the next couple of weeks, including more information about new curriculum offerings and 2024 instructors!
Click here to sign-up or learn more!
Lincoln Douglas Debate
Tournament Results
This weekend, LD debaters competed at three bid tournaments: the Isidore Newman Invitational, the Ridge Debates, and the Paradigm at Dowling Catholic.
Congratulations to Durham’s Sachin Aggarwal for winning the 2023 Isidore Newman Invitational. In finals, Sachin defeated Harrison’s Brooke Schiano-Gonzalez on a 5-0 decision (Fleming, Haber, Lugo, Ogundare, Randall). Additional congratulations to Little Rock Central’s Charlie Swan for being the top speaker.
Full pairings and results can be found here.
Congratulations to Horace Greeley’s Salma Gheith for winning the 2023 Ridge Debates. In finals, Salma defeated Princeton’s Amanda Sun on a 2-1 decision (Chen, Lee, Smith*). Additional congratulations to Salma for being the top speaker.
Full pairings and results can be found here.
Congratulations to Lakeville South’s Ezana Haile for winning the 2023 Paradigm at Dowling Catholic. In finals, Ezana defeated Lincoln East’s Jeremy Moussoli on a 3-0 decision (Rosenberg, Webb, White).
Full pairings and results can be found here.
Public Forum Debate
Tournament Results
This weekend, PF debaters competed at three bid tournaments: the Isidore Newman Invitational, the Ridge Debates, and the Paradigm at Dowling Catholic.
Congratulations to Andrew Johnson & Kieran Kelly from Carrollton for championing the 2023 Isidore Newman Invitational. In finals, they defeated Annie Bovitz & Isabel Goldfarb from Bronx Science on a 3-2 decision (Hoffmann, Savoy, Agho-Otoghile, Hernandez, Knatt). Additional congratulations to Christopher Columbus’ Joseph Alonso for being the top speaker.
Full pairings and results can be found here.
Congratulations to Helen Mancini & Astrid Harrington from Stuyvesant for championing the 2023 Ridge Debates. In finals, they defeated Eric Liu & Andrew Liu from Scarsdale on a 2-1 decision (Sun*, Akridge, Copeland). Additional congratulations to Stuyvesant’s Astrid Harrington for being the top speaker.
Full pairings and results can be found here.
Congratulations to Austin Siefken & Edison Zheng from Lakeville North for championing the 2023 Ridge Debates. In finals, they defeated Eric Liu & Andrew Liu from Scarsdale on a 2-1 decision (Sun*, Akridge, Copeland). Additional congratulations to Stuyvesant’s Astrid Harrington for being the top speaker.
Full pairings and results can be found here.
Best of luck to everyone competing next weekend! Stay tuned for future tournament results.
How to Master a Topicality 2NR
by Elmer Yang
Topicality is one of the most visible and present arguments in the current LD debate meta-game. In nearly every round involving a “spec” Aff that affirms a subset of the resolution, some version of Topicality makes it into the 1NC ranging from grammatical interps about “bare plurals” to the wide variety of “x verb” in the resolution means all or prohibits exceptions. However, the omnipresent nature of these arguments on nearly every topic incentivizes the proliferation of “no plans” backfiles with the same 10-15 responses to the “Neg Interp creates infinite PICs” standards and generic “limits DA” extension. This has made going for and judging these types of Topicality debates to be extremely stale and difficult to listen to. Despite this, a well-done T 2NR can combine the impressiveness of technical theory debating with a robust substantive understanding of the topic that can impress even the most die-hard “yes subsets” judges. This article aims to break down the fundamental components of Topicality and generic meta-tips for how to elevate a Topicality 2NR off the doc and to a higher-level.
Topicality can be broken down into three basic components.
First, the interpretation - what types of Affs are included in your model, and implicitly, what broad categories of Affs does your model exclude? This should always include a definition of some word in the resolution, whether it comes from a court case, a legal analysis, or even a grammatical reading of a particular word like x word is a bare plural. It is extremely important to have some definitional grounding to justify your interpretation. No topic is truly built equal, and there is always a way to make the topic more fair for the negative by simply debating something not about the topic. For instance, the theoretically most fair topic might be “Resolved: vanilla is better than chocolate” because it avoids the uniqueness issues or lit biases that often plague negatives on topics. While more fair in a vacuum, it would be near impossible for anyone to reasonably predict the Aff to affirm vanilla is better than chocolate simply by looking at the text of the resolution. Justifying a model of debate based purely on what is more fair, as opposed to grounding it in the resolution, is a slippery slope to no topic at all, since teams in different situations can just move the goalpost.
Second, the violation - what about the Aff’s plan text does not fit within your model of debate? For the most part, establishing a violation, if your interp is crafted well, is simple. For example, it can be as simple as your argument that the Aff cannot condition a right and the Aff establishes a right to housing for just native tribes. Other times, the violation can be less obvious just by looking at the plantext. The plan might use the exact wording of the resolution, but the actual solvency advocate and internal links are about something entirely non-topical. This begs the question of “plantext in a vacuum”, which is the idea that when determining the topical nature of an Aff, do we look at just the wording of the plantext, or something else. The arguments for plantext in a vacuum usually involve the argument that allowing the Neg to cherry-pick lines from the 1AC’s solvency advocate or internal links creates infinitely regressive T debates, since every Aff would become untopical, leading to debaters becoming incentivized to be T specialists, as opposed to substantively debating the Aff. The arguments against plantext in a vacuum involve the idea that the words in the plan mean nothing without context (being that of the advantage) and that if the Aff gets offense off of the “functional” implementation of the plan (in their solvency advocate or internal links), the Neg should be able to test the topical nature of that offense as well. One of the most important tips in a plan text in a vacuum debate as the negative is if you think that plan text in a vacuum is bad, you need a counter-way to determine Topicality violations whether that being from the 1AC tags or 1AC solvency advocate. Otherwise, you leave yourself very exposed to the infinite regress argument made above.
Finally, the standards - why should the judge prefer your model of debate? This is where the impact component of Topicality comes into play, and where you’ll see the discussion of “limits” or “ground”. If you include a model of debate where the Aff is topical, how does that explode the numerical amount of Affs under the topic beyond a predictable or stable degree, and what negative arguments does the negative lose access to under that model? This is also where you hear the discussion of “precision” as an impact, which is simply a way to package predictability as an internal link stemming from the reading of a word in the topic (or whether or not a definitionally-grounded interp is more precise), as opposed to simply predictability from an explosion of the quantitative amount of affirmatives.
One other component of Topicality debates is that of paradigm issues, namely that of using competing interpretations (offense-defense) or reasonability (does the impact to the abuse story’s differential between the Aff’s counter-interp and the Neg’s interp outweigh the loss of substance debating from the Neg going for Topicality, as opposed to a substantive strategy). While this can be an important frame for evaluating Topicality debates, this is also a subject that cross-references with a lot of Theory debating which is talked about in numerous articles online, including this one, so I will not further expand upon it in this article.
Now that we’ve established the core portions of Topicality, here are some broad meta-tips that will elevate your Topicality 2NRs:
1] Specificity is your best friend. When you are talking about limits or ground, this is a place where your 2NR absolutely cannot be recycled from topic to topic. You need to be giving topic-specific examples over what exact ground that you’ve lost and why the Aff’s model hurts your ability to access that ground. Aff teams win T debates when the Neg is talking about limits or ground in a vacuum, with no relevance to this topic and this model. This necessitates identification of offensive case-lists which is a list of Affs that the Aff’s model would include that you believe are bad, in addition to a list of generic negative positions that you lose access to. This also naturally makes impact calculus easier to resolve in your favor since the judge now has a material abuse story to quantify the impact that you are going for. Too often, 2NRs are lazy and simply say “Politics” as their ground story. Not only is the loss of the Politics DA almost never true in any model (and in some judges’ eyes, perhaps a good thing to lose), the loss of a trans-topic generic is never a persuasive argument. T debates, at their best, can be extremely technical, but that shouldn’t lose the fact that Topicality is about a substantive model of the topic, and you need to do that work to substantively articulate it.
2] Focus less on the impact and more on the internal link. One fallout of heavy Topicality backfile recycling is that debates tend to be very focused on the extremes of “limits is the most important impact and outweighs everything” or “overlimiting categorically outweighs underlimiting”. Neither the Aff or Neg’s interp will ever entail either one or infinite Affs. The 2NR needs to do work in terms of contextualizing your offense to the differential between your interps, not an extreme caricature of the Aff’s model tantamount to “unlimited Affs”. “Limits” cannot categorically outweigh, especially on a variety of different topics with different literature bases and different parameters. Similar to how not all warming impacts are the same (as they have different scenarios for solving), talk less about limits in isolation, and more about how the Aff’s specific model unlimits and hurts the negative. When teams in the 1AR lean too hard into the extremes of the negative’s model, being able to develop your internal link and contextually weigh can help avoid some of the Aff’s draconian characteristics of your interpretation.
3] When describing your model, think about 1AC -> 1NC and 1NC -> 1AR interactions as opposed to arguments in a vacuum. Too often, teams will simply talk about the existence of arguments to articulate their offense i.e. PICs, small subset Affs, etc. This is a first-level step that’s important, but can often result in two teams passing each other through the night as they list out positions that are difficult for a judge to resolve on whether the existence or loss of certain positions is worse. Just saying the Neg’s model gives the Neg “infinite PICs” is in and of itself not an argument. You need to explain why a lack of unified solvency deficit that the 1AR can make to the type of PICs they allow outweighs their limits or ground arguments. The best T debaters will articulate how certain positions affect the opponents in a series of moves and countermoves. A model of debate by definition involves understanding clash, and articulating the strength or weakness of clash under your model is important, especially for impact comparison reasons.
4] Precision and debate-ability are not as clean cut as they are often made out to be. When learning Topicality, the question of precision is often one of the most difficult concepts to grasp. When debaters use precision (they might also use short-hand like “predictability” or “semantics”), they are merely saying that the best way for determining a model of debate is accurately determining the meaning of the words in the resolution. For example, to prove that a definition is more precise, debaters will sometimes use cards that talk about “plain meaning” or when people define definitions used ordinarily and without context. In doing so, they will argue that absent a card that explicitly defines a word in the exact context of the resolution, the only “precise” way to read it is through its meaning used ordinarily. In contrast, when debaters talk about debate-ability (or pragmatics), they are referring to impacts within the parameters of debate, namely the existence of certain arguments (ground) that affect the education, clash, and fairness within a debate round.
Many times, debaters will treat these concepts on completely different layers. Doing so can result in ceding ground when you don’t have to. Obviously, when talking about precision, the impact to being accurate is being predictable, which then ties back to questions of preparation and quality of clash. While precision can certainly be a very strong internal link to predictability, it is definitely not the only one, and you can still leverage non-precision arguments to get an in-roads to the external link of predictability while arguing that precision is not a terminal impact but simply an internal link to a more debate-able topic. Similarly, when talking about debate-ability, it would be ludicrous for a team to say that only the parameters of debate’s unique game mechanics matter and that the resolution is irrelevant. They merely make the argument that precision can only go so far, that it is impossible to leverage academic and legal verbose for determining the usefulness of definitions in debate’s context, and doing so would ultimately be more imprecise. By viewing these arguments as interconnected internal links rather than terminals on different layers, it can make resolving these weighing mechanics easier.
5] When weighing definitions, establish a metric you think is best and impact that out to why it matters. When concerned with matters of precision, debaters will often throw around terms like “legal context”, “plain meaning”, or “intent to define” without ever explaining why one metric of determining the precise meaning of a word matters more than the other. My advice here is instead of shotgunning five different reasons why your interp could be good, pick one or two and explain why using it is best for determining a model of debate. Just because you are in a Topicality debate as opposed to a disad/case debate does not void the necessity to resolve questions of whose framing metric matters more. Using the example of “intent to define”, which debaters will use to describe cards that look to actually outline what a word means and “norm” it, as opposed to cards where authors will use phrases loosely and colloquially (or instances that are used “for the purpose of this law/article”), one can argue that definitions that intend to define a word are the most predictable since other people will use and rely on those definitions, thus conforming the literature, whereas, if they use it too loosely or for a particular purpose, other people won’t model or rely on those definitions.
When you delve into the weeds of Topicality, it can be a daunting endeavor to navigate the specific jargon and nuances of executing a good Topicality 2NR. However, once you understand and master the form of what Topicality looks like, the skills immediately transfer from topic to topic (even though your blocks should not) and will quickly become one of the most useful generics in your negative arsenal.
Elmer Yang is a current FinTech Business Analyst who graduated from the University of Texas-Austin with a degree in Business Honors. He debated at Mckinney Boyd High School in Texas and has been a debate coach for 6 years coaching both Policy and LD. Elmer’s students have received over 100 bids to the TOC and won the TOC, MSTOC, New York City Invitational, Glenbrooks, Mid America Cup, and Harvard Westlake.