We have found that novices to this new field of study need basic features by which they can identify or recognize a systems process on new scales, or from different science disciplines. While students often want just a word definition because it makes the new concept more comfortable. Normally, that is how we usually learn new terms. But the cluster of identifiers (attributes; characteristics; features) give them the ability to “discover” the common pattern when comparing many different particular, scalar, instantiated, real systems. Identifying features capture the essential characteristics of a systems process. We think it is better to have this “cluster of features” understanding than to memorize a word definition. It is less limited and more easily updated or unlearned. We believe strongly in the importance of unlearning.
Sometimes two ISPs share one or two ID Features; but that overlap just indicates that all ISPs interact and does not interfere with discriminating between them as each has several unique identifiers. That SPs are individual but highly interconnected mimics the neural net structure of the brains we use to understand systems. So the initial learning events for SPT entails becoming familiar with the ID Features for each SP. For example, some of the Identifying Features for the SP hierarchy-forming processes would be: (i) scales/levels; (ii) subunit to unit; (iii) clustering; (iv) gaps; (v) emergence; (vi) seven cross-level parameter trends; (viii) subsumption; and more.
Research leading to a new curriculum to attract students to STEM, Integrated Science General Education (ISGE) reinforced our decision to rely on clusters of features rather than word definitions which are often contaminated by language or previous limited (for ISGE; see also our detailed website on this program, is-ge.org)