Classification is the systematic assignment of resources to a system of intentional categories, often institutional ones.
(See §7.1, “Introduction”)
A classification system is foremost a specification for the logical arrangement of resources because there are usually many possible and often arbitrary mappings of logical locations to physical ones.
A classification creates structure in the organizing system that increases the variety and capability of the interactions it can support.
Classifications are always biased by the purposes, experiences, professions, politics, values, and other characteristics and preferences of the people making them.
Three types of bias in technical systems are pre-existing, technical, and emergent bias.
Classification schemes in which all possible categories to which resources can be assigned are defined explicitly are called enumerative.
When multiple resource properties are considered in a fixed sequence, each property creates another level in the system of categories and the classification scheme is hierarchical or taxonomic.
Classification and standardization are not identical, but they are closely related. Some classifications become standards, and some standards define new classifications.
A standard is a published specification that is developed and maintained by consensus of all the relevant stakeholders in some domain by following a defined and transparent process.
Standard semantics are especially important in industries or markets that have significant network effects where the value of a product depends on the number of interoperable or compatible products.
The principle of literary warrant holds that a classification must be based only on the specific resources that are being classified.
(See §7.2.2.1, “Principles Embodied in the Classification Scheme”)
The uniqueness principle means the categories in a classification scheme are mutually exclusive. Thus, when a logical concept is assigned to a particular category, it cannot simultaneously be assigned to another category.
(See §7.2.2.2, “Principles for Assigning Resources to Categories”)
The general solution to satisfying the uniqueness principle in library classifications when resources do not clearly fit in a single category is to invent and follow a detailed set of often-arbitrary rules.
(See §7.2.2.2, “Principles for Assigning Resources to Categories”)
Categories sometimes change slowly, but they can also change quickly and radically as a result of technological, process, or geopolitical innovation or events.
(See §7.2.2.3, “Principles for Maintaining the Classification over Time”)
Flexibility, extensibility, and hospitality are synonyms for the degree to which the classification can accommodate new resources.
(See §7.2.2.3, “Principles for Maintaining the Classification over Time”)
Bibliographic classification is distinctive because of a legacy of physical arrangement and its scale and complexity.
Faceted classification systems enumerate all the categories needed to assign resources appropriately, but instead of combining them in advance in a fixed hierarchy, they are applied only if they are needed to sort resources with a particular combination of properties.
Facets should be independent dimensions, so a resource can have values of all of them while only having one value on each of them.
Top-level facets should be the properties that best differentiate the resources in the classification domain. The values should be of equal semantic scope so that resources are distributed among the subcategories. Subfacets of “Cookware” like “Sauciers and Saucepans” and “Roasters and Brasiers” are semantically balanced as they are both named and grouped by cooking activity.
Facet values must accommodate potential additions to the set of instances. Including an “Other” value is an easy way to ensure that a facet is flexible and hospitable to new instances, but it not desirable if all new instances will be assigned that value.
Most tagging seems insufficiently principled to be considered classification, except when tags are treated as category labels or when decisions that make tagging more systematic turn a set of tags into a tagsonomy.
A task or activity-based classification system is called a taskonomy.
Supervised learning techniques start with a designed classification scheme and then train computers to assign new resources to the categories.