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dbt-labs · Nov 7, 2023 · c590ec0 · c590ec0
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diff --git a/website/docs/collaborate/govern/model-access b/website/docs/collaborate/govern/model-access
diff --git a/website/docs/guides/best-practices/how-we-mesh/mesh-1-intro.md b/website/docs/guides/best-practices/how-we-mesh/mesh-1-intro.md
@@ -12,11 +12,11 @@ To date, there also hasn't been a first-class way to effectively manage the depe
 
 That's where **dbt Mesh** comes in - empowering data teams to work *independently and collaboratively*; sharing data, code, and best practices without sacrificing security or autonomy. 
 
-This guide will walk you through the concepts and implementation details needed to get started. dbt Mesh is not a single product: it is a pattern enabled by a convergence of several features in dbt:
+This guide will walk you through the concepts and implementation details needed to get started. dbt Mesh is not a single product - it is a pattern enabled by a convergence of several features in dbt:
 
 - **[Cross-project references](/docs/collaborate/govern/project-dependencies#how-to-use-ref)** - this is the foundational feature that enables the multi-project deployments. `{{ ref() }}`s now work across dbt Cloud projects on Enterprise plans.
 - **[dbt Explorer](/docs/collaborate/explore-projects)** - dbt Cloud's metadata-powered documentation platform, complete with full, cross-project lineage.
-- **Governance** - dbt's new governance features allow you to manage access to your dbt models both within and across projects.
+- **Governance** - dbt's governance features allow you to manage access to your dbt models both within and across projects.
   - **[Groups](/docs/collaborate/govern/model-access#groups)** - groups allow you to gather together nodes in your dbt DAG that are logically connected (such as by functional area) and assign an owner to the group.
   - **[Access](/docs/collaborate/govern/model-access#access-modifiers)** - access configs allow you to control who can reference models.
   - **[Model Versions](/docs/collaborate/govern/model-versions)** - when coordinating across projects and teams, we recommend treating your data models as stable APIs. Model versioning is the mechanism to allow graceful adoption and deprecation of models as they evolve.