Glossary

Acceptance rate. The share of model suggestions that curators accept or edit, per field, task, and model.

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.

AI-use policy. The one-page statement, approved at the top of the organization, of which tasks a model may assist, which uses are prohibited, which controls apply, and who owns each instrument.

Automated share. The share of responses coded at or above the confidence threshold without a coder; measured by a re-coded sample each round.

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

Component register. The table with one row per AI component the organization runs: owner, model, provider, data, human review, risk tier, approval and review dates, and exit plan.

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.

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

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).

Decision point. The step at which a named person decides on a model's output (a code, a proposal, an explanation, a draft).

Derived variable. A variable computed from others by a stated rule, for example labour force status from the employment questions.

Disclosure control. The measurement and reduction of the risk that a released file identifies a person or business or reveals something about one: removal of identifiers, top-coding, coarsening of geography, suppression of rare combinations; applied before any release.

Edit rule. A machine-readable condition a record must satisfy (range, consistency), owned by the organization and applied before and after any model proposal.

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.

Estimated. The status of an extracted value for which the document offers no check: no printed label, total, or derivation.

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.

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

Grounding. Restricting a language model to what was retrieved or given for the task: an answer that uses only the retrieved records, a draft that uses only what the record and its named sources support.

GSBPM. The Generic Statistical Business Process Model, the UNECE reference model of the phases and sub-processes of statistical production, from specifying needs to disseminating and evaluating.

Harmonization. The matching of mention variants to canonical identifiers.

Incident. An AI component's output reaching a reviewer, a product, or a user with an error that the controls should have caught, or that a control caught at the last step; handled by a fixed procedure and recorded.

ISCO-08. The International Standard Classification of Occupations, whose four-digit codes classify jobs; the label of the coding examples in this guide.

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.

LLM. Large language model. A model that generates text, used here for drafting metadata, answering questions, and grading.

Manifest. A file that lists the parts of something so that it can be checked or resumed: for a job, the records and their status; for a release, every file with its checksum; for an agent interface, the design document of its tools, inputs, outputs, examples, and resources.

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

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

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

Metadata Editor. The World Bank's open-source application for documenting data of all the types in the World Bank schemas, with templates and validation, publishing to NADA, and export to SDMX and, for some data types, schema.org, Croissant, and DCAT.

Method of record. The documented, reproducible imputation or estimation method the organization applies; a model proposes and explains beside it.

Number check. The comparison of every figure in an assistant's answer with the values of the source table, which marks each as verified or unverified before the answer is used.

Open-weight model. A model whose weights can be downloaded and run on the organization's own infrastructure, under a published licence that may be permissive or restricted.

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.

Prompt. The instructions and context given to a language model for one task; kept as a versioned file when it is part of a workflow.

Question bank. A repository of question wordings keyed by the concept each measures and the surveys that use each.

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

Re-coded sample. A random sample of the automated share coded blind by staff each round to measure the automated accuracy.

Report card. A run's scores per slice with intervals, the set version, and the component versions, published with a release.

Required score. The suite score a task's model has to reach, set from the cost of error before any candidate is tested.

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

Roles. What a model may do in production: flag, propose, code, explain, draft. It does not decide.

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).

Statement of model use. The section of a release's quality report that says where models were used, which, how they performed, who decided, and what the effect was.

Structured output. A model's answer returned as data in a declared shape (a JSON object with named fields that conforms to a schema) so that a script can validate, read, and aggregate it.

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

Switch-off test. The test that production proceeds with the model layer stopped, with the fallback per task exercised.

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.

Token. The unit in which language models read and write text, roughly three quarters of a word; prices, context limits, and throughput are counted in tokens.

Universe. The population a variable applies to, as the question was asked.

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

Verified. The status of an extracted value that matched a printed label, a total, a derivation, or a catalog value.

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.