The Gigamap Interview with Birger Sevaldson
Bibliographic record
Abstract
As an introduction to the official RSD10 systems map exhibition opening on Wednesday, November 03 at TU Delft, we invited Professor Birger Sevaldson to reflect on the practice of gigamapping and how it has evolved since its origination. \n \nGigamapping is a practice that has become well known in the RSD community. System mapping or synthesis mapping are similar practices that have also gained popularity in the past years. The practice originated at the Oslo School of Architecture and Design around 2006, with the work of Birger Sevaldson. Since then, the practice has been openly shared with other communities, and it has been adopted by many different groups. \n \nIn this year’s RSD exhibition, there are 17 contributions from seven different institutes. There are maps from Carnegie Mellon University (US), the National Institute of Design (India,) Istanbul Technical University (Turkey), Delft University of Technology (NL), OCAD University (Canada), Cardiff University (UK), and Savannah College of Art and Design (US). This year’s topics are diverse, from systems of responses to the recent pandemic, systems affected by climate change, to maps that take a critical look at the systems of designers (and students) themselves.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.020 | 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".