InfoTorg from past to present: On a large Swedish online service and its role in the online market
Bibliographic record
Abstract
The Swedish online market is quite consolidated and stable. Nearly half of the revenues come from financial services, and about one quarter from credit information services. Excluding these, InfoTorg of Sema InfoData is the largest online service, providing 140,000 users with access to about 25 online services, some by means of gateways. Based on an overview of the Swedish online market, including facts on revenues from the largest actors, this article describes the role of InfoTorg in this market, from past to present. It concludes that though challenged from several directions, the present role will last. The effects of information which is freely available on the Web will be limited, partly due to the established role of InfoTorg in the infrastructure of Swedish information provision, especially as regards official information. InfoTorg is an odd bird in an IT company, the advantages of support for advanced IT solutions in technology‐intense information business seems to more than compensate for the possible drawbacks. Continuous investments in existing and new services demonstrates the ability of Sema InfoData and InfoTorg to challenge the increasingly fierce competion from national as well as international actors in the Swedish online market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".