Glossary

Adapter. A small set of trained parameters added to a model for a task, so that fine-tuning fits on modest hardware (parameter-efficient fine-tuning).

Agent. An AI system that calls tools to complete a task; here an assistant that calls the organization's data API or MCP server to answer a question about statistics.

AI visibility check. A fixed set of questions asked of AI assistants on a schedule, recording whether the organization is cited and whether the figures are right.

AI-ready. Data and metadata that software, including AI systems, can find, retrieve, interpret, and cite without manual steps. See the AI-ready data framework.

Annotation. Optional properties on a tool that describe its behaviour (for example that it is read-only); hints that clients treat as untrusted unless the server is trusted.

API. Application programming interface: the address at which a program asks a system for data and receives it in a structured form.

Benchmark (population). The share of each group in the population, from the census or population estimates, against which a dataset's composition is compared.

Candidate. A model shortlisted for a task, with its licence classified and its card checked, to be run on the suite.

Canonical name. The one name, with its acronym and identifier, under which a dataset's mentions are counted.

Co-use. The use of another organization's dataset in the same document as the organization's own.

Confidence. A number between 0 and 1 that a model returns with its output to say how sure it is; the basis of a threshold rule.

Confidence threshold. The model confidence above which a code is accepted automatically, set from the measured accuracy curve.

COUNTER Code of Practice for Research Data. Rules for logging and reporting dataset views and downloads, including the separation of machine access from regular access.

Coverage. The share of a reference list of known uses that the collected documents contain.

Crosswalk. A mapping from one classification to another, for example from a national occupation classification to ISCO.

DataCite. The registration agency and metadata schema for dataset DOIs. The World Bank Microdata Library assigns DataCite DOIs.

DDI. The Data Documentation Initiative, the standard for documenting surveys and their variables, value labels, and files; the World Bank microdata schema is based on it.

Decision. A curator's recorded verdict on a suggestion: accept, edit (with the final text), or reject (with a reason).

Document record. The catalog record of a document in the World Bank document schema, with the series and surveys it draws on.

DOI. Digital object identifier, a persistent identifier registered with DataCite that resolves to a dataset or to one version of it, carries citation metadata, and makes citations countable.

Embedding. A list of numbers that a model assigns to a text so that texts with similar meaning have similar numbers; the basis of search by meaning.

Feature. A column a model may use as input.

Fine-tuning. Changing a model's weights by training it further on the organization's own labelled examples, so that it does one task better or follows the organization's conventions.

Flagged. The status of an extracted value that failed a check and awaits correction with the page open.

Gap. The distance between a dimension's current level and its target, measured in levels and in the number of questions below the target's value.

Gate. The rule that decides whether a change ships, applied to a paired comparison of the evaluation suite before and after the change.

Harmonization. The matching of mention variants to canonical identifiers.

Inventory. The list of documents with their type, year, page count, text-layer status, and counts of tables and figures.

Issue. The review pipeline's output: a detected problem with a category, a severity from 1 to 5, the current value, and a proposed value.

Label. The column a model is trained to predict: the occupation code, the transcribed value, the series a question refers to.

Labelled sample. Sentences labelled by a person with the dataset mention they contain, or none, used to measure the extractor.

Language model. A model that reads and writes text; used in these guides to draft, flag, code, explain, and translate, with a person deciding.

Machine client. Software, including an AI assistant or agent, that retrieves data from the organization's API or MCP server and accepts the terms of use for doing so.

Map. The table of model-assisted tasks with phase, role, review, data sensitivity, model location, and owner.

Match type. How a mention was matched: exact, phrase (contains), fuzzy, semantic, or none.

Maturity level. One of three states used in this guide: foundational, AI-ready, AI-native.

MCP. The Model Context Protocol, the open standard through which AI applications discover and call tools, read resources, and use prompts offered by a server, such as a statistics API.

Mention. A reference to a dataset in a document, named or unnamed.

NADA. The open-source data catalog from the International Household Survey Network, used for the World Bank Microdata Library. It supports DDI Codebook, Dublin Core, ISO 19115/19139, and IPTC. NADA AI, a service being integrated into NADA, adds semantic search and an MCP server over the catalog.

Named-entity extraction. A model that finds spans of text referring to entities of a given type, here dataset mentions, with a confidence.

OCR. Optical character recognition: reading text from the image of a page, which is what a scanned document needs before anything can be extracted from it.

Precision. The share of extracted mentions that are correct.

Precision and recall. Precision is the share of what a system found that is right; recall is the share of what is there that the system found. F1 is the average that combines the two.

RAG. Retrieval-augmented generation. A pattern where the system retrieves records first and the model writes the answer from them.

Recall. The share of labelled mentions that the extractor found.

Reference list. Known uses of the data, assembled from staff knowledge and citation tracking, against which coverage is measured.

Resource. Context a server offers to the model or the user, such as code lists or a release calendar, identified by a URI.

Review board. The interface in which a curator sees the current and proposed values as a diff and decides.

SKOS and XKOS. SKOS is the web standard for publishing controlled vocabularies (concepts with labels and identifiers); XKOS extends it for statistical classifications and their correspondences.

Split. The division of a dataset into a training part (the model learns from it), a validation part (the model builder chooses settings on it), and a test part (touched once, to report a score).

Suite. A question set, the scripts that score it, the required scores, and the record of runs.

Text layer. The machine-readable text inside a PDF; absent in scanned documents until OCR produces it.

Threshold. The value of a score (a confidence, a similarity, a garble rate) at which the rule changes what happens to an item; set from measurements.

Trace. The logged record of one question: tools called, series used, answer, whether the agent declined, latency.

Typology of use. The distinction between mention and use, and among primary, secondary, and background use.

Use. A document whose analysis, decision, or statement depends on the data, as opposed to a mention that cites them as context.

Variant. A way of naming a dataset other than its canonical name: an acronym, an informal name, a translation, a misspelling.

Weight. A variable that scales each record to the population, with the calibration and the estimates it applies to documented.

Weight (survey). The number of population members a record stands for in a survey, kept as a column for population estimates; not a training weight by default.

World Bank metadata schemas. JSON Schema definitions published by the Development Data Group for indicators, microdata, documents, geospatial data, tables, images, scripts, and videos, used by its catalogs and the Metadata Editor.

Zero-shot model. A model that performs a task from a description of the labels alone, without examples of that task in its training.