AbstractThe thesis investigates the properties of two trends or time series which formed a:part of the Co-Citation bibliometric model "X~Ray Crystallography and Protein Determination in 1978, 1980 and 1982". This model was one of several created for the 1983 ABRC Science Policy Study which aimed to test the utility of
bibliometric models in a national science policy context. The outcome of the validation part of that study proved to be especially favourable concerning the utility of trend data, which purport to model the development of speciality areas in science over time. This assessment could have important implications for the use of such data in policy formulation. However one possible problem with the Science Policy Study's conclusions was that insufficient time was available in the study for an in-depth analysis of the data.
The thesis aims to continue the validation begun in the ABRC study by providing a detailed.examination of the characteristics of the data contained in the Trends numbered 11 and 44 in the model. A novel methodology for the analysis of the
properties of the trends with respect to their literature content is presented. This is followed by an assessment based on questionnaire and interview data, of the ability of Trend 44 to realistically model the historical development of the field of
mobile genetic elements research over time, with respect to its scientific content and the activities of its community of researchers.
The results of these various analyses are then used to evaluate the strenghts and weaknesses of a trend or time series approach to the modelling of the activities of scientifiic fields. A critical evaluation of the origins of the discovered strengths
and weaknesses.in the assumptions underlying the techniques used to generate trends from co-citation data is provided. Possible improvements. to the modelling techniques are discussed.
|Date of Award||Oct 1988|
|Supervisor||David Collingridge (Supervisor) & Harry Rothman (Supervisor)|
- co-citation analysis
- bibliometric modelling
- time series