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AI Methodology / Data + Measurement

From a concept to a reliable measure.

A shared method for building reliable text data and extracting specific beliefs, opinions and attitudes at scale. Human judgment defines the task. AI applies the defined task across the corpus.

A pencil marks a text passage beside a laptop.
Human judgmentClose reading sets the annotation standard.

01 / Data foundation

Preserve the source. Prepare the evidence.

Preserve the original text and its provenance. Create clean, linked research views from that record. Keep source identity and publication time attached throughout the process.

Capture

Original records

Store source text, stable identifiers and publication timestamps. Retain the original record for later review.

Prepare

Consistent inputs

Standardize formats, identify duplicates and record missing content. Keep transformations explicit.

Link

Research context

Connect text to the relevant asset, author and publication date. Preserve the keys needed for aggregation.

Version

A stable reference

Freeze source snapshots. Document coverage and transformations for each data release.

Source recordsPreserved archiveLinked research viewsVersioned measurement

02 / Measurement workflow

Define the task before choosing the model.

Explicit rules and human-labeled examples make a qualitative concept measurable. A held-out benchmark tests whether the model follows those rules. Evaluate the model before applying it at scale.

Measurement design · Process diagram
From a definition to corpus-wide measurementDefine the concept, build a human-labeled corpus and separate training data from a reserved benchmark. Fine-tune on training data. Test the fixed model against the benchmark. Move to large-scale inference after the quality evaluation. 01 / DEFINITIONResearch conceptTarget and decision rules 02 / ANNOTATIONAnnotated corpusReviewed human labels TRAINING SPLITTask examplesUsed for fine-tuning 04 / FINE-TUNINGDomain modelFixed before evaluation 03 / BENCHMARKHeld-out labelsKept out of training EVALUATIONEvaluationPrecision · Recall · F1 05 / INFERENCECorpus-wide labelsVersioned and resumable The definition sets the task.Human judgment supplies the examples.AI applies the task at scale.

Split the corpus into training and evaluation sets. Keep the benchmark separate during development. Apply the fixed model at scale after it passes evaluation.

01 / Define

Specify the concept

Start with the research question. Define the target, the labels and the evidence needed for each decision. State how ambiguous cases should be treated.

OutputA versioned definition and annotation guide.

02 / Annotate

Establish reference labels

Sample real language from the target setting, including difficult expressions and varied text lengths. Annotate using the guide, review disagreements and retain the final decisions.

OutputA reviewed corpus of texts and labels.

03 / Benchmark

Reserve an evaluation set

Keep evaluation examples separate from training. Include the labels and difficult cases relevant to the task. Fix the benchmark and scoring rules before final evaluation.

OutputHeld-out examples, labels and evaluation criteria.

04 / Fine-tune

Fit the model to the task

Fine-tune on the training split. Use development data for model and prompt choices. Fix those choices before evaluation. Test the model on the reserved benchmark and inspect errors by class.

OutputA task-specific model and an evaluation report.

05 / Infer

Apply the validated model

Freeze the model and task rules. Run the corpus in resumable batches. Link labels back to their source records. Aggregate by the unit required for the research question.

OutputTraceable text labels and documented aggregates.

03 / Inference at scale

Scale the process. Keep each result traceable.

Large-scale inference needs the same discipline as model development. Preserve the task rules, identify every output and track what enters each aggregate.

Server racks line a data-center aisle.
Inference infrastructureA fixed task, applied across a large corpus.

Consistency

A fixed measurement task

Version the definition, model and inference settings together. Validate implementation changes against reference outputs.

Continuity

Resumable processing

Process the corpus in batches. Record completed work and verify outputs. After an interruption, resume from the last completed batch.

Interpretability

Documented aggregates

Join labels to source records. Define the aggregation unit and denominator. Report coverage, missing labels and sample dates.

A traceable resultText IDTargetPublication timeLabelModel versionSource snapshot

04 / Research measures

One method. Distinct measurement targets.

The workflow supports different concepts. Each measure has its own definition, labels and evaluation criteria.