Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
When a binary label hides multiple distinct reasons, split…
When a binary classification hides multiple distinct failure modes or reasons within a single bucket, decompose it into separate dimensions that can be evaluated independently.
When forced into a binary decision, document the full…
When forced to make a binary decision after spectrum-based deliberation, document the richer multi-dimensional signal alongside the binary outcome so future analysis can recover what the compression discarded.
Test every classification for MECE
For every pair of categories in a classification system, verify that no item can legitimately belong to both (mutual exclusivity test), and verify that no domain item falls outside all categories (collective exhaustiveness test).
Match classification architecture to domain
Design multi-class classification systems with mutually exclusive categories when items can only be one type, and multi-label systems when items can legitimately belong to multiple categories simultaneously.
Define each priority level with a one-sentence operational…
Write a one-sentence decision rule for each priority type that defines membership criteria operationally before applying the types to any backlog.
If more than 20% of items are top priority…
Ensure that highest-priority items constitute less than 20% of total backlog; if more items are marked critical, recalibrate threshold definitions to restore differentiation.
Every priority system needs an explicit 'not now' tier…
Include an explicit 'not now' or lowest-tier priority type to prevent deferred items from inflating middle categories and to create visible records of deliberate exclusion.
Before high-stakes categorization, name the actions…
Before committing to a category assignment in high-stakes decisions, explicitly name what actions that category triggers and what you would do differently if the item belonged to an adjacent category.
Classification friction reveals missing categories — map…
When items consistently resist classification in your system (you hesitate, force-fit, or leave uncategorized), map what those resistant items have in common to diagnose missing categories that represent dimensions you care about but haven't encoded.
Audit categories against your values — missing values…
For each top-level category in your knowledge system, write one sentence explaining what value that category protects or promotes, then identify missing categories that would operationalize values you hold but aren't currently encoding.
A growing 'Miscellaneous' category signals missing…
When a 'Miscellaneous' or 'Other' category grows faster than named categories, it signals that your classification dimensions are missing a meaningful distinction that reality contains.
Design hierarchy levels by what question each answers…
Before creating or reorganizing any hierarchical structure, ask what question each level answers—top level for domain identification, leaf level for specific item selection, and intermediate levels for navigation steps between.
Structural depth encodes real boundaries; bureaucratic…
Distinguish structural hierarchy depth (encoding real containment or inheritance) from bureaucratic depth (added for perceived tidiness) by asking what would break if you removed each level—keep only levels whose removal would destroy actual functional boundaries.
Clustered overrides mean the parent's assumption is wrong…
When multiple children override the same inherited property, restructure the hierarchy rather than accumulating individual overrides, as clustered overrides indicate the parent's assumption is systematically wrong.
Override traits, not identity — if you are overriding…
Override properties rather than identities—preserve the child's relationship to its parent category while modifying specific inherited traits, as overriding identity signals you need a different category, not an override.
Classify decisions as speed-dominant or accuracy-dominant…
Before any analysis begins for a decision, explicitly classify it as speed-dominant (reversible, low cost of wrong, high cost of delay) or accuracy-dominant (irreversible, high cost of wrong, low cost of delay), then let that classification dictate process—fast decisions get 15 minutes and bias toward action, slow decisions get structured analysis.