Transparency

What is automated

• Data collection: 29 datasets from 10 public-sector providers are synced automatically on schedule — Skolverket daily, SCB and Kolada monthly. The status of each data source is on the settings page.
• Calculations: Merit scores, SALSA scores, eligibility rates and all other key metrics are calculated deterministically from source data. Calculation logic is documented on the method page.
• Page generation: All pages are generated automatically whenever the site is published. No pages are manually edited after generation.
• Archiving: Data is automatically versioned with timestamps at every update. See the versioning policy.
• Quality control: An automatic check compares the record count in new data with the previous version and blocks publication if the count drops by more than 20%.

What is manual

• Editorial content: Interpretation guides, glossary, data stories and this page — written by Markus Reimer.
• Reasonableness checks: Automatic checks are supplemented with manual review during large data changes.
• Corrections: Reported errors are investigated and fixed manually. See the corrections policy.
• Preschool geodata: Preschools without coordinates in Skolverket's register are matched against SCB's geodata and OpenStreetMap's Nominatim. About 85% have been matched.
• School Inspectorate data: Supervisory results, injunctions and fines are synced quarterly on schedule, plus via manual trigger when needed.

Potential biases and limitations

• No selection: All open school units in Sweden are included (dormant and discontinued excluded) — there is no selection that could create systematic bias.
• Timing: Data may be 1–12 months old depending on the source. Grade data refers to the previous school year and is published with a delay.
• Statistical confidentiality: Skolverket's confidentiality rule (<10 pupils for pupil results, <3 full-time positions for staff statistics) hides data for small schools. This means small schools systematically lack more data points.
• Methodology choices: Choice of SALSA model, weighting in the school choice guide and aggregation methods are documented but involve unavoidable design decisions. See the SALSA method and method policy.
• School survey: Response rates vary widely between schools. Results with fewer than 5 responses are not shown; remaining values should be read together with response rate and respondent count.

Independence and conflicts of interest

Skolkoll is operated by Skolspegeln AB (company reg. no. 559359-7288). For transparency, it is declared that Markus Reimer works professionally at AcadeMedia, Sweden's largest independent education provider.

Skolkoll is an independent project with no resourcing, editorial control or influence from that employer. Source data comes from public registers and data services and is processed identically for all schools and providers; rights and attribution vary by source.

If a school provider wants to "look better" on Skolkoll, there is only one recipe: deliver good quality that is reflected in the public statistics.

Open data

• Skolkoll data files can be downloaded without an account. The codebase is not currently published as open source, but calculation methods and data sources are documented openly on the method page.
• Skolkoll grants no blanket sublicence to third-party data or processing without express approval.
• Source data follows each source's terms. See the versioned source and licence matrix.

AI on Skolkoll

AI is used in a few, well-defined places — chiefly the assistant Kollen, but also to stylise school images and as internal drafting aids. The AI sets no published numbers; all statistics are computed deterministically from source data. Controls are specific to each flow. For school images, a human reviews the source, licence and absence of identifiable people before model processing. An approved source image may then be stylised and displayed automatically without a separate post-review of the result. Kollen is a conditional, clearly labelled AI chat that you choose to use. New responses are legally stopped until the evidence of the provider's processing country is complete and we have confirmed that the block in the code is deployed in the production environment; this page does not claim a verified active production state. Conditional internal AI drafts are reviewed by a human before external use. SeeHow Skolkoll uses AI for a full account of where AI is used, what controls are in place and what the AI does not do.

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