Digital Borderlands: Cultural Studies of Identity and Interactivity on the Internet
Bibliographic record
Abstract
All books are collective projects. This one was the result of the actual collectiveresearch project Digital Borderlands, funded by the Swedish Councilfor Research in the Humanities and Social Sciences, whose support wasabsolutely crucial for its success. Additional support came from the SwedishTransport and Communications Research Board, as well as from the National Institutefor Working Life program for Work & Culture in Norrköping, where theproject had its administrative basis. The project organized an international workshopthere in spring 2000, and the invited keynote speakers Brenda Danet, SteveJones, Nina Lykke, and Terje Rasmussen were all important to us, as were all theother thirty participants, mainly from the Nordic countries. Steve Jones’ generousoffer to include this book in his series was particularly wonderful, and it has been agreat pleasure to work with Sophy Craze and her colleagues at Peter Lang Publishers.We finally wish to thank all others who have offered us support, inspirationand information, including informants, colleagues, and friends all over the onlineand offline globe. Johan Fornäs, Kajsa Klein, Martina Ladendorf,Jenny Sundén, and Malin Sveningsson
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".