Editorial & Corrections Policy

How Pages Are Produced

PlainWorkforce's occupation, industry, and employer pages are generated from published federal datasets: the U.S. Bureau of Labor Statistics 2024–2034 Employment Projections (employment levels, projected growth, and median wages), the U.S. Department of Labor's O*NET database (task, skill, and knowledge profiles for each occupation), and SEC and BLS industry-occupation distributions used to estimate employer workforce mixes. We download each source directly, load it into a structured database, and render every page from that database. The employment, wage, and growth figures you see are BLS's numbers — not hand-typed and not estimated by us.

This is a data-publishing model: the same template renders hundreds of occupation and industry pages so that every entity is covered consistently. We are transparent that these data pages are produced programmatically from the source datasets rather than written individually. The editorial work goes into the pipeline — how data is sourced, normalized, joined, and computed — into the methodology, and into the written guides; not into hand-authoring near-identical entity pages, which would add no accuracy and invite inconsistency.

How AI-Exposure Scores Are Derived

The AI-exposure score is our own analysis, not an official government figure. We compute it from O*NET task-importance data: each occupation's tasks are assessed for susceptibility to current and near-future AI automation — routine data processing, pattern recognition, and structured analysis score higher; physical dexterity, in-person care, and complex human judgment score lower — and combined into a 0–100% score weighted by task importance. The full method is documented in our methodology. Because it is a derived estimate, we present it as PlainWorkforce's analysis of O*NET data, clearly distinct from BLS's published employment and wage figures, and we make no claim that any specific job will or will not be automated.

Sourcing Standards

• Primary sources only. Employment, growth, and wage figures come from the BLS Employment Projections and Occupational Employment and Wage Statistics programs; task data comes from O*NET — as documented in our methodology.
• Attribution in context. Each data page names its dataset and reference period (BLS 2024–2034) near the figures and links to the methodology.
• Derived values are labeled. Numbers we compute ourselves — AI-exposure scores, percentile rankings, sector aggregates — are presented as our analysis, distinct from BLS's published figures.
• No invented data. Where a value is unavailable for an occupation, the page says so rather than filling the gap with an estimate.

Update Cadence

BLS publishes its Employment Projections every two years and its wage statistics annually; O*NET updates on a rolling release schedule. When new source data is released we refresh our database and recompute derived metrics, typically within a few weeks. Between releases the figures are stable because the source itself does not change. The reference period (2024–2034) is shown on the data pages.

Corrections Process

If a figure on PlainWorkforce looks wrong, please tell us. Because our pages are generated from published datasets, a genuine error almost always traces back to either the source data or our processing of it — so this is how we handle a report:

1. Report. Email corrections@plainworkforce.com or use the contact page with the page URL and the number that looks off.
2. Verify. We compare the figure against the original BLS or O*NET record for that occupation, industry, or period.
3. Fix at the source. If the value is wrong on our side, we correct it in the database and pipeline that generate the page — not just on the single page — so every affected page is fixed at once. If the figure faithfully reflects the source data, we explain that and, where useful, add context.
4. Note it. Material corrections are reflected the next time the page rebuilds, with the data reference period shown so you can see which release a page is based on.

We aim to acknowledge data-error reports within a few business days.

Editorial Independence

PlainWorkforce is an independent publisher and is not affiliated with the BLS, the Department of Labor, or any employer it covers. We do not accept payment, sponsorship, or promoted placement from any company, school, or other covered entity. Our only revenue is contextual display advertising served by Google AdSense; advertisers do not influence which occupations, industries, or employers we cover or how we present data. Our rankings and AI-exposure scores are computed mechanically from source data, so no entity can pay to move up a list.

Appropriate Use

PlainWorkforce is for informational purposes only and does not constitute career, financial, or legal advice. AI-exposure scores are estimates of automation susceptibility, not predictions that any individual job will be eliminated, and employment projections are BLS's modeled outlooks, not guarantees. For decisions about your career or education, treat this data as one input alongside your own circumstances and qualified guidance. See our full appropriate-use disclaimer.