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Explainer Desk Journalist Toolkit

The explainer desk turns complexity into clarity. Whether the subject is a new law, a Budget measure, a scientific finding or an economic shift, your job is to make it accurate, understandable and genuinely useful — without oversimplifying, without false balance and always anchored to primary sources. This toolkit covers the sourcing discipline, data honesty and accuracy standard the role demands.

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What the explainer desk journalist does

An explainer desk journalist practises explanatory and service journalism: taking a subject that is genuinely hard — policy, law, science, economics, technology — and making it clear enough for a non-specialist reader to understand and act on. The work is reader-led. You start from the questions people are actually asking, answer them in a logical order, define terms as you go, and link every claim to the evidence behind it.

The craft sits at the intersection of accuracy and accessibility. Explainers are read as reference material and cited long after publication, so the accuracy bar is high and the temptation to oversimplify is the main enemy. The accuracy hub and the Data Journalism hub are your two closest allies.

A typical explainer workflow

Commissioned or reactive, most explainers move through the same stages.

  1. 1
    Define the question. Pin down exactly what the reader needs to understand and why it matters now, before writing a word.
  2. 2
    Gather primary sources. Go to the legislation, official data, consultation, court judgment or peer-reviewed study rather than secondary write-ups.
  3. 3
    Assess the evidence. Weigh where the strong evidence sits and where genuine uncertainty or disagreement remains, so you can weight the piece honestly.
  4. 4
    Structure for clarity. Order the explainer around the reader questions, define jargon on first use and lead with what matters most.
  5. 5
    Visualise honestly. Add charts, tables or diagrams only where they make an accurate point faster than prose, with source and date labelled.
  6. 6
    Date and set review. Record when the facts were last checked and set a trigger to refresh the page when the law, figures or consensus change.

Core skills

Translating complexityRendering law, science and economics into plain English without losing the meaning.
Primary-source literacyReading legislation, data releases, consultations and studies confidently and citing them precisely.
Weighing evidenceJudging where the strong evidence sits so you can weight coverage to it and avoid false balance.
Data and numeracyHandling statistics, percentages and base rates honestly, and spotting a misleading figure.
Honest visualisationBuilding charts and diagrams that clarify rather than mislead, with sources on the graphic.
Maintenance disciplineKeeping evergreen explainers current with dated reviews and transparent corrections.

Key accuracy and ethical risks

  • 1
    Oversimplifying to the point of inaccuracy. Clarity is the goal, but stripping out the caveats that actually matter turns an explainer into a misleading one. Keep the load-bearing nuance and cut the jargon instead. The systematic accuracy guide sets out how.
  • 2
    False balance on settled evidence. Giving a fringe claim equal weight to an established consensus misleads readers about where the evidence sits. Weight coverage to the evidence, not the volume of the voices. This is an accuracy obligation under the IPSO Editors Code Clause 1.
  • 3
    Misleading charts and statistics. Truncated axes, cherry-picked baselines and percentages without absolute numbers can distort an otherwise accurate story. Follow the data-visualisation design and chart-truthfulness guidance.
  • 4
    Stale evergreen pages and undisclosed AI use. An explainer that is no longer current is a liability, and AI drafting tools can introduce plausible-sounding errors. Date and review your pages, and follow the AI-generated content standards on verification and disclosure.

Curated guides for the explainer desk

APIs for journalistsPulling official data straight from source APIs to anchor explainers in primary numbers.Systematic approaches to accuracyStructured checking routines to keep complex explainers correct.Data-visualisation designDesigning charts that clarify honestly rather than mislead.AI-generated contentVerification and disclosure standards when using AI drafting or research tools.Data storytellingBuilding a clear narrative around numbers without distorting them.Statistics for journalistsBase rates, averages and significance, and how to report figures accurately.Chart truthfulness and ethicsThe specific traps that turn a chart into a misleading one.IPSO Editors' CodeClause 1 accuracy and the distinction between fact and comment.

Tools for the explainer desk

Headline Checker
Test explainer headlines for absolute claims, passive voice and IPSO Clause 1 accuracy.
Source Verification Checklist
Confirm a claim traces back to a genuine primary source before you publish it.
All interactive tools
The full set of pre-publication and accuracy tools on UK JournoHub.

Templates that save you time

Editor Standards PackHouse-style starter, corrections policy and editorial policy for consistent explainers.Media Law Risk PackPre-publication accuracy and right-of-reply templates for explainers that name people.

FAQs for explainer desk journalists

What makes a good explainer different from a news story?
A news story tells the reader what just happened; an explainer tells them what it means and why it matters. The explainer desk starts from the questions a reasonable reader is actually asking, then answers them in plain language, in a logical order, with each claim tied to a primary source. Good explainers are honest about uncertainty, define jargon on first use, and resist the temptation to oversimplify a genuinely contested subject into a false certainty. The accuracy standard is high because explainers are read as reference material and are often cited long after publication, so they must be right and stay right.
How do I avoid false balance in an explainer?
False balance is presenting two positions as equally supported when the evidence is not evenly weighted. It is a particular hazard on science, health and climate explainers, where a fringe claim can be given the same airtime as an established consensus. The fix is to weight coverage to the evidence, not to the loudness of the voices: state where the strong evidence sits, explain the basis for it, and describe genuine disagreement accurately without inflating it. Being clear about the strength of evidence is not taking sides; it is the accuracy obligation the IPSO Editors Code Clause 1 already places on you.
How should I source and link to primary sources in an explainer?
Trace every factual claim back to its origin and link the reader there. For policy, that means the legislation on legislation.gov.uk, the official consultation or the House of Commons Library briefing rather than a secondary write-up. For statistics, cite the Office for National Statistics or the relevant department and state the date of the data. Linking to primary sources lets readers check your work, protects you if a claim is challenged, and builds the trust an explainer depends on. Where you rely on an interview or a document, describe what it is and why it is authoritative.
How do I keep evergreen explainers accurate over time?
Explainers age badly when the world moves and the page does not. Build a review cadence: date every explainer, record when the underlying facts, figures or law were last checked, and set a review trigger for events likely to change them such as a Budget, a new Act coming into force or a fresh data release. When you update, correct transparently and note the change rather than silently overwriting. Treat the review date as a promise to the reader that the page reflects the current position, and prioritise the explainers that carry legal, financial or health consequences if they fall out of date.
How do I use data and charts honestly in an explainer?
Charts persuade quickly, which is exactly why they must be honest. Start bar-chart axes at zero, avoid truncating scales to exaggerate a change, choose the chart type that fits the data rather than the one that looks dramatic, and label the source and date on the graphic itself. Show absolute numbers alongside percentages where a small base could mislead, and never present a correlation as a cause. The data-visualisation design guidance on this site and the chart-truthfulness principles cover the specific traps, and a good visual should make an accurate point faster than the prose, not a misleading one.

Career path and progression

Explainer and service desks have grown as digital newsrooms have discovered how well explanatory content builds trusted, returning audiences. Journalists on the desk often deepen into a specialist correspondent role — economics, science, health or technology — where the same clarity and sourcing skills apply to original reporting, or move sideways into data journalism as their comfort with figures grows.

The maintenance mindset and primary-source discipline also make explainer journalists strong candidates for standards and verification roles. If you want to specialise, compare the data journalist, science correspondent and business correspondent toolkits.

The primary references an explainer desk should link to first.

Primary sources

Where to next

The Data Journalism hub builds the numeracy an explainer desk lives on, the Visual Journalism hub covers honest charts and graphics, and the Ethics hub anchors the accuracy standard your explainers are judged against.

Go to Data Journalism hub →

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