The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Work through priority types sequentially rather than scanning the full list, consulting only the highest-priority tier that contains items and never reviewing lower tiers until higher tiers are empty.
Type along both urgency and importance dimensions simultaneously to counteract the mere urgency effect, creating explicit categories for important-but-not-urgent items that would otherwise be chronically deprioritized.
Balance role type distribution across teams rather than optimizing for individual capability, because team performance depends on covering necessary functions not maximizing any single dimension.
Audit classification systems for duplicates, dead categories, overstuffed catch-alls, and ambiguous labels on a regular schedule to prevent compound interest on classification debt.
Audit your classification system by identifying what's prominent (operational values), what's absent (neglected values), and what's miscellaneous (blind spots), because category structure reveals value structure.
For high-stakes classifications, explicitly name adjacent categories and assess asymmetric costs of each error direction before committing to action protocols.
Set review triggers for important classifications—time-based, evidence-based, or outcome-based—because categories that persist without verification inherit compounding error from initial misclassification.
Calculate your actual prediction accuracy across documented tests rather than relying on felt sense of how often you're right, because subjective confidence systematically exceeds objective accuracy.
Reclassify when boundary cases become the majority, when two categories collapse into functional synonyms, or when a single category becomes an undifferentiated catchall, because these signal that current categories no longer carve reality at its joints.
Add categories for values you hold but haven't operationalized, and remove categories that exist by inertia, because classification systems calcify around past values and make un-encoded values invisible.
When using AI tools that classify on your behalf, explicitly examine whose values are embedded in the AI's classification categories, because automated classification at scale makes embedded values operationally invisible.
Design categories around prototypical central examples rather than exhaustive definitional rules, as prototype-based classification matches cognitive processing speed and flexibility better than rule-checking.
Test category systems with boundary cases that resist clean classification, as items at category edges reveal missing dimensions, vague boundaries, and incorrect framing more reliably than central members.
Log items that resist classification rather than forcing them into nearest categories, as accumulation of misfits reveals systematic problems in category structure that individual cases obscure.
Decompose classification into independent facets when items genuinely belong to multiple categories simultaneously, rather than forcing single-parent assignment.
Test each candidate classification dimension by asking whether it enables a question you actually ask and cannot currently answer - dimensions that don't resolve real queries add maintenance cost without retrieval benefit.
Audit what your classification system discards by explicitly listing the information lost in each category compression, then check whether any discarded dimensions matter for decisions the system supports.
Adjust classification granularity when categories either collapse distinctions needed for decisions (over-compression) or preserve distinctions never used for decisions (under-compression).
Evolve classification systems through specific operations (splitting, merging, renaming, promoting, retiring) triggered by measurable signals (category size imbalance, unused categories, hesitation in classification).
Treat classification systems as living infrastructure that evolves rather than finished products, recognizing that domains change, understanding deepens, and purposes shift over time.
When classification purpose shifts (from organizing by topic to organizing by project, or from product area to customer segment), redesign the primary organizational dimension rather than forcing the old structure to answer new questions.
Map relationships between concepts as explicitly as you map the concepts themselves, because insight emerges from the connections rather than from individual nodes.
Externalize assumed relationships by writing them down with explicit entities, relationship type, and evidence basis, because implicit connections cannot be examined, tested, or corrected.
State the relationship type (causal, correlational, temporal, enabling, inhibiting) separately from the entities being related, because different relationship types license different inference patterns and conflating them produces systematic errors.