Bibliographic record
Abstract
A model is an abstract representation of reality. It can be an object, a system or an idea. In general terms, one could say that a model is a simplification of reality. Modeling is a fundamental and quantitative way to understand complex phenomena and systems. Modelling make up a scientific approach that can be applied to analyse a wide range of physical and social problems. Modelling of complex systems is becoming increasingly a common practice in virtually different disciplines, giving rise to active fields of studies such as mathematical modelling, econometrics, social modelling, computational physics, chemistry, mechanics, and biology, to name just a few. Through modeling one can readily cross over from one discipline to another, the basic concepts and techniques are relatively the same. Computational models are useful tools for representing abstractions and concrete realities. Computational models are intended to provide knowledge about social and technical aspect of systems and their users. They are capable of providing computer systems designers and research analysts with rich insights to build processes, procedures and tools to support systems operations in order to adapt these operations to peoples’ technology needs. This Chapter presents an overview of computational modelling. It provides examples of computational models types and how they are currently used to inform our understanding of issues connected to users and computer systems. The goal of the Chapter is to present the reader with the background knowledge necessary for understanding the Bayesian computational approach presented in this book and to draw their attention to think about ways in which modelling can be used to analyse and understand problems in other social systems.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".