Bibliographic record
Abstract
“Netplexity - Networks and Complexity for the Real World” General talk on Networks and Complexity given by Dr Tim Evans, Theoretical Physics, Imperial College London on Friday 10th August 2012 http://imperial.ac.uk/people/t.evans or search for “Tim Evans Networks” Second annual Student Conference on Complexity Science Oxstalls Campus, University of Gloucestershire 9th - 12th August 2012. http://bccs.bristol.ac.uk/events/SCCS/ Abstract:Title:- Netplexity:- Networks and Complexity for the Real World I will look at some of the different ways the science of Complex Networks gives insights into the world around us. Networks are an excellent way to look at the many large data sets which have appeared over the last decade or so such as web pages, Facebook, digital document repositories. The explosion of interest in networks has produced many new tools and insights which can be applied to many different types of data, from biological systems to humanities. Bio Dr Tim Evans is part of the Theoretical Physics group and the Complexity and Networks programme at Imperial College London. He finished his PhD at Imperial in 1987, applying statistical physics to quantum field theory for many body problems. He was then a researcher at the University of Alberta in Edmonton Canada, after which he held research positions at Imperial, including a final one as a Royal Society University Research Fellow. He was appointed to the staff at Imperial in 1997. He has always been interested in many body systems both in and out of equilibrium and his current focus is on complex systems in general and Complex Networks in particular. This is both from a theoretical perspective (such as line graph representations of networks) and in terms of applications to practical problems such as bibliometrics and cultural transmission, part of an interest in `sociophysics' in general. This includes an ongoing project in Archaeology.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".