Gigamapping Rapid Changes in Working Life: Service designing a new service for new labour and welfare administration in Norway
Bibliographic record
Abstract
The world has been challenged in the last several years with wicked problems such as COVID-19, the Ukrainian war, and global warming. These phenomena create impacts on our society and the services we design. The Norwegian Labour and Welfare Administration (NAV) has become conscious that—to help individuals better—it is strategically essential to help the companies and institutions that employ individuals themselves. Services created for the companies could lessen the impact of massive layoffs or resignations. Still, it is good to bear in mind that when someone in the marketplace might be losing their position, others may be gaining it, and this way may need a rapidly new working force, e.g. the companies that are run by fossil fuels are losing market place to players that are investing in green energy. As part of the Master’s Service Design Futures course at the Oslo School of Architecture and Design, students created one gigamap as a class to create a shared understanding of how these impacts influence the micro, meso, and macro level, the services that NAV wishes to create in the future. The mapping applies a structure from Geels’ Multi-level Perspective (Geels, 2011) to understand how the impacts create transitions and how they could be handled in the services designed—an approach that has not yet been widely explored in scientific literature. Also, the process of gigamapping showed how the mapping itself can be a good starting point for a service design process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".