Diffusing software product and process innovations : IFIP TC8 WG8.6 Fourth Working Conference on Diffusing Software Product and Process Innovations, April 7-10, 2001, Banff, Canada
Bibliographic record
Abstract
Preface. Message from the General Chair. Telling an Innovation Story E. Burton Swanson. Part I: Implementation and Coordination Issues. A Web Innovations on Software Process-Center for Diffusing Techniques C. Freericks. (BLANCK PURPER) Diffusion and Adoption of IT Products and Processes in a Danish Bank J. Pries-Heje, S. Tryde. Part II: New Interpretations of Diffusion Theory. The Phenomenon of Diffusion T.J. Larsen. A Perspective of the Innovation-Diffusion Process from the Self-Organizing System T. Mitsufuji. The Illusion of Diffusion in Information Systems Research T. McMaster. Part III: Software Process. Understanding and Changing Software Organizations K.H. Kautz, H. Westergaard Hansen, K. Thaysen. Diagnosing Diffusion Practices Within a Software Organization I. Andersson, K. Nilsson. Part IV: Contextual Factors. The Diffusion of Components R. Veryard. Across the Divide: Two Organisations Form a Virtual Team and Codevelop a Product L. Levine, G. Syzdek. What's Wrong with the Diffusion of Innovation Theory? K. Lyytinen, J. Damsgaard. Part V: Communication of Information. Influences of Sources of Communication on Adoption of a Communication Technology W.D. Stuart, T. Callawat Russo, H.E. Sypher, T.E. Simons, L.K. Hallberg. Knowledge Creation in Improving a Software Organisation P. Pourkomeyluian. Part VI: Experience Reports. How To Live With Software Problems K.J. Jeppesen. Introducing Concurrent Functional Programming in the Telecommunications Industry B. Dacker. Change and Adaptive Behavior in Organizations A.P. Jansma. In Search of an Efficient EDIcebreaker H. ZinnerHenriksen. Process Definition in Web-Time A.S. Koch.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".