The target is a reasoning operation, not a list of clever phrases
A phrase becomes useful when the learner knows the job it performs. “The issue is not X; it is Y” can help reframe a problem. “That would follow if X were true” can test an assumption. “The evidence points to X, but it does not establish Y” can separate observation from conclusion.
The set therefore begins with Moves such as reframing, calibrating confidence, tracing causes or comparing trade-offs. Frames are then reviewed as language-specific ways to perform those Moves.
This is language training, not a claim to teach better thinking automatically
A well-phrased sentence cannot make an argument sound if the underlying reasoning is weak. Thinking in Language does not claim to turn linguistic Frames into a cognitive upgrade. It gives learners expressions that make certain distinctions easier to state and inspect.
That distinction matters. The educational value comes from practising language for reasoning tasks, while the quality of the reasoning still has to be judged on evidence, logic and context.
Frames become more valuable when contrasted
Reasoning language often contains nearby choices: cautious versus strong claims, cause versus trigger, disagreement with a premise versus disagreement with a conclusion. A set can put those options side by side and ask the learner to choose the one that matches the intended stance.
This is where the Metkagram domain model helps: Move identifies the job, Frames offer realizations, Contrasts state the decisive difference and Choices require retrieval. The result is a small decision system rather than a page of “smart phrases.”