Fix inconsistencies

_pubs/kaleidoscope.md

@@ -27,7 +27,7 @@ videos:
 materials:
   - name: Code
     url: /pubs/kaleidoscope.zip
-    type: file
+    type: code
 teaser: 'Kaleidoscope’s workflow consists of identifying meaningful examples, generalizing them into larger, diverse sets representing important concepts, and using these concepts to specify and test model behavior.'
 ---
 Desired model behavior often differs across contexts (e.g., different geographies, communities, or institutions), but there is little infrastructure to facilitate context-specific evaluations key to deployment decisions and building trust. Here, we present Kaleidoscope, a system for evaluating models in terms of user-driven, domain-relevant concepts. Kaleidoscope's iterative workflow enables generalizing from a few examples into a larger, diverse set representing an important concept. These example sets can be used to test model outputs or shifts in model behavior in semantically-meaningful ways. For instance, we might construct a “xenophobic comments” set and test that its examples are more likely to be flagged by a content moderation model than a “civil discussion” set. To evaluate Kaleidoscope, we compare it against template- and DSL-based grouping methods, and conduct a usability study with 13 Reddit users testing a content moderation model. We find that Kaleidoscope facilitates iterative, exploratory hypothesis testing across diverse, conceptually-meaningful example sets.

_pubs/rich-screen-reader-vis-experiences.md

@@ -33,7 +33,7 @@ videos:
 materials:
   - name: Supplementary Material
     url: /pubs/rich-screen-reader-vis-experiences/supplementary-material.zip
-    type: file
+    type: file-archive
   - name: Olli (Open Source Library)
     url: https://mitvis.github.io/olli/
     type: cube

_pubs/viral-visualizations.md

@@ -27,6 +27,6 @@ materials:
     type: book-open
   - name: Supplementary Dataset & Analysis Scripts
     url: /pubs/viral-visualizations/supplementary-material.zip
-    type: file
+    type: file-archive
 ---
 Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government's pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.

_pubs/vistext.md

@@ -20,7 +20,7 @@ materials:
     type: cube
   - name: Code
     url: https://github.com/mitvis/vistext
-    type: cube
+    type: code
 videos:
   - name: ACL Teaser Video
     key: Ygyw6AQIPUo