tighter contrast with target based success

Signed-off-by: Nathaniel <[email protected]>

Author: NathanielF

examples/case_studies/bayesian_sem_workflow.ipynb

@@ -62,7 +62,9 @@
     "  - Each step asks: Does this addition honor theory? Improve fit?\n",
     "  - Workflow = constant negotiation between parsimony and fidelity.\n",
     "\n",
-    "These approaches complement one another. We'll see how the iterative and expansionary approach to model development is crucial for understanding the subtleties of SEM models. How our understanding grows as we track their implications across increasingly expressive candidate model structures."
+    "These approaches complement one another. We'll see how the iterative and expansionary approach to model development is crucial for understanding the subtleties of SEM models. How our understanding grows as we track their implications across increasingly expressive candidate model structures.\n",
+    "\n",
+    "In many settings, statistical modelling has become a game — a contest of metrics, accuracy scores, and leaderboard performances. Each modelling project promises improvement, but only within the narrow boundaries the metric defines. Our rich, complex aims of scientific discovery, understanding, and generalisable insight are replaced by simplified numbers that are easy to compare and optimize. The result is a kind of methodological gamification: we chase scores instead of comprehension. This is the contrast case against which we set Bayesian workflow. We'll make the case below to treat statistical modelling not as simplistic competition, but as a rewarding craft. A craft that underpins the entire scientific enterprise; our most successful epistemological project."
    ]
   },
   {
@@ -7016,7 +7018,7 @@
     "\n",
     "We have now seen how to articulate Structural Equation models and their variants in PyMC. The SEM workflow is, at heart, Bayesian in temperament: construct then check. Check then refine. Refine then expand. Hypothesise and estimate. Estimate and assess. Both disciplines reject the checklist mentality of “fit once, report, move on.” Instead, they cultivate a focused, deliberate practice. Each discipline forces an apprenticeship where skill is developed. Skill to handle how assumptions shape understanding and how the world resists impositions of false structure. Skill to find the right structures. Each iteration is a dialogue between theory and evidence. At each juncture we ask whether this model speaks true? Whether this structure reflects the facts to hand. \n",
     "\n",
-    "In the end, the value of craft in statistical modeling lies not in improving benchmark metrics, but in the depth of understanding we cultivate through careful communication and justification. The Bayesian workflow reminds us that modeling is not the automation of insight but its deliberate construction. Our workflow is a process of listening, revising, and re-articulating until the model speaks clearly. Like any craft, its worth is measured not by throughput but by fidelity: how honestly our structure reflects the world it seeks to describe. Each diagnostic, each posterior check, each refinement of a latent path is a form of attention — a small act of resistance against the flattening logic of metrics and checklists. These are the constructive thought processes that drive job-satisfaction. __To practice modeling as craft is to reclaim pride in knowing what our models say, what they do not say, and what they imply.__ To find, in that discipline and skilled attention, the satisfaction of meaningful work.\n"
+    "In the end, the value of craft in statistical modeling lies not in improving benchmark metrics, but in the depth of understanding we cultivate through careful communication and justification. The Bayesian workflow reminds us that modeling is not the automation of insight but its deliberate construction. Our workflow is a process of listening, revising, and re-articulating until the model speaks clearly. Like any craft, its worth is measured not by throughput but by fidelity: how honestly our structure reflects the world it seeks to describe. Each diagnostic, each posterior check, each refinement of a latent path is a form of attention — a small act of resistance against the flattening logic of target metrics and checklists. These are the constructive thought processes that drive job-satisfaction. __To practice modeling as craft is to reclaim pride in knowing what our models say, what they do not say, and what they imply.__ To find, in that discipline and skilled attention, the satisfaction of meaningful work.\n"
    ]
   },
   {

examples/case_studies/bayesian_sem_workflow.myst.md

@@ -63,6 +63,8 @@ The structure of the SEM workflow mirrors the Bayesian workflow closely. Each st
 
 These approaches complement one another. We'll see how the iterative and expansionary approach to model development is crucial for understanding the subtleties of SEM models. How our understanding grows as we track their implications across increasingly expressive candidate model structures.
 
+In many settings, statistical modelling has become a game — a contest of metrics, accuracy scores, and leaderboard performances. Each modelling project promises improvement, but only within the narrow boundaries the metric defines. Our rich, complex aims of scientific discovery, understanding, and generalisable insight are replaced by simplified numbers that are easy to compare and optimize. The result is a kind of methodological gamification: we chase scores instead of comprehension. This is the contrast case against which we set Bayesian workflow. We'll make the case below to treat statistical modelling not as simplistic competition, but as a rewarding craft. A craft that underpins the entire scientific enterprise; our most successful epistemological project.
+
 ```{code-cell} ipython3
 import warnings
 
@@ -1530,7 +1532,7 @@ This two-step of information compression and prediction serves to concisely quan
 
 We have now seen how to articulate Structural Equation models and their variants in PyMC. The SEM workflow is, at heart, Bayesian in temperament: construct then check. Check then refine. Refine then expand. Hypothesise and estimate. Estimate and assess. Both disciplines reject the checklist mentality of “fit once, report, move on.” Instead, they cultivate a focused, deliberate practice. Each discipline forces an apprenticeship where skill is developed. Skill to handle how assumptions shape understanding and how the world resists impositions of false structure. Skill to find the right structures. Each iteration is a dialogue between theory and evidence. At each juncture we ask whether this model speaks true? Whether this structure reflects the facts to hand. 
 
-In the end, the value of craft in statistical modeling lies not in improving benchmark metrics, but in the depth of understanding we cultivate through careful communication and justification. The Bayesian workflow reminds us that modeling is not the automation of insight but its deliberate construction. Our workflow is a process of listening, revising, and re-articulating until the model speaks clearly. Like any craft, its worth is measured not by throughput but by fidelity: how honestly our structure reflects the world it seeks to describe. Each diagnostic, each posterior check, each refinement of a latent path is a form of attention — a small act of resistance against the flattening logic of metrics and checklists. These are the constructive thought processes that drive job-satisfaction. __To practice modeling as craft is to reclaim pride in knowing what our models say, what they do not say, and what they imply.__ To find, in that discipline and skilled attention, the satisfaction of meaningful work.
+In the end, the value of craft in statistical modeling lies not in improving benchmark metrics, but in the depth of understanding we cultivate through careful communication and justification. The Bayesian workflow reminds us that modeling is not the automation of insight but its deliberate construction. Our workflow is a process of listening, revising, and re-articulating until the model speaks clearly. Like any craft, its worth is measured not by throughput but by fidelity: how honestly our structure reflects the world it seeks to describe. Each diagnostic, each posterior check, each refinement of a latent path is a form of attention — a small act of resistance against the flattening logic of target metrics and checklists. These are the constructive thought processes that drive job-satisfaction. __To practice modeling as craft is to reclaim pride in knowing what our models say, what they do not say, and what they imply.__ To find, in that discipline and skilled attention, the satisfaction of meaningful work.
 
 +++