Bibliographic record
Abstract
Invited talk given by Tim Evans (Imperial College London) at the EPSRC Workshop on "Scaling in Social Systems” held at the Saïd Business School, Oxford on 1st December 2011. Abstract: The pattern of innovation seen through citations of academic papers has long fascinated academics. It has been known for at least fifty years that the data shows various long tailed distributions. In this talk I will look at some of the features of the data and show how to extract some simple universal patterns. I will discuss some of the implications of the results and some of the further questions it raises. •What is a citation? •What does an individual citation mean? •Is the data perfect? •Why citation count? •If not citation count, what else? •What does this data say about me? •Why h-index? •What is a self-citation? •How else can I use this data? •How will things change? Tim S. Evans – Mini Biography Tim studied the mixture of quantum field theory and statistical physics in his PhD at Imperial College London. He was supervised by Prof. Ray Rivers who also supervised another speaker, Prof. Luis Bettencourt. Tim then spent time as a researcher at the University of Alberta in Edmonton Canada, before returning to research positions back here at Imperial, latterly as a Royal Society University Research Fellow. He was appointed to a lectureship at Imperial in 1997. Around 2003 he expanded his work on statistical physics to cover at problems in complexity, with a particular interest in network methods. This has included participation in an EU collaboration with social scientists on innovation, ―ISCOM, run in part by Prof. Geoff West (another speaker today). This fuelled his interest in social science applications and started an on going collaboration with an archaeologist.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 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".