2-12-2010, KIMO

Eva Ortoll / Àlex Lopez

Knowledge codification and abstraction. Scientometric

approaches

Context and Objetives

Based on the I-Space theoretical framework, we try to explain the knowledge codification and abstraction of a specific research discipline by scientometric techniques, the idea is to gain knowledge in the use of these techniques to apply them to the ATLAS project (in its documents, databases, etc) at CERN.

The objective is to obtain an objective way to measure knowledge codification and abstraction at ATLAS experiment, completing and comparing the results with those obtained with the interviews.

Research Questions

Rq1: How codified is the knowledge in a scientific discipline?

Rq2: How abstract is the knowledge in a scientific discipline?

Rq3: How we can explain knowledge creation and diffusion in a scientific discipline?

Data gathering

We will work with the ISI database (in this sense we would retrieved the complete register of an article in terms of: authors, title, abstract, source, references, etc)

We have decided to choose three disciplines to apply the techniques: Physics, Management and Information Science

Initial approach: Mapping Knowledge Domains

“Knowledge mapping is locating a knowledge item (a node in a scalable network) within the categorization scheme developed for a given knowledge domain” (Boisot).

Mapping knowledge domains (MKD) are used for making and visualizating the structure and dynamics of disciplines or networks (Borner, 2003; Boyack 2004), and different techniques are used for it.

Because we work, initially, with a bibliographic dataset ,we’ll focus those related with literature/science mapping.

Science mapping can be done at several levels of granularity: disciplines (information science), research fields (science studies), research subfields (scientometrics) and research topics (citation analysis; scientific collaboration) (Besselaar, 2006).

A research front allow us to obtain a picture of the main current research topics in a discipline, how the topics are related with each other and how they evolve over the time. Research fronts provide insights in how new scientific knowledge is incorporated into existing research (Upham 2010, Small 2003, Kuhn 1970).

We propose to work with the smallest unit of analysis, the “research topic” as an expression of a “knowledge item” within a scientific discipline, even we are aware that a “research topic” is a simplistic way to look at the knowledge item or knowledge assets. Perhaps, a second step analysis of the information around a “research topic” will allow us to define better what a KA is in the context of scientific publications.

Approaches Main idea

Bibliographic coupling (Kessler, 1963)

links documents that reference the same set of cited references

It is assumed that bibliographic coupling gives the more dynamic and “actual” structure of a research front.

Co-citation analysis (Marshakova, 1973, Small, 1973):

links documents that are cited together.

It is assumed that co-citation gives the background of a research front.The use of co-citation only has been criticized to draw a research front (lost of some topics, super citing effect, predominance of theoretical and review papers....)

Co-word or word-co-ocurrences (Callon, 1986).

Semantic similarity of research fields.

It has been criticized as and indicative of a research front because the “words” can adopt different meanings depending on the context they are used, also with questions related to polysemus words or words with little semantic value (Leydesdorff 2004, 2010; Janssen 2008). Some new approaches (comibining clustering and factoring) are used to solve polysemy and synomy problems (Kawkkle 2009).

Combination of citation-based approaches with text-based approaches (Zitt, 2010; Janssens, 2008; Leydesdorff 2004, Noyons 1999 )

Co-occurences of words in references, or in titles and references. Several combinations are used.

It is assumed the co-word highlighted better the new concepts, and the citations the knowledge background. The hybrid method allows to draw a more real delineation of research fronts

From Boyack; K; Klavans, R. JASIST 2010

* useful to analysis the degree of abstraction?

Approaches Main idea

Field to field citations *(Buter et al 2010)

Citations form one scientific field to another are used to measure interdisciplinary connections

Useful to identify the creation of a new discipline, but also to identify knowledge that is generated in one field and is used in another one*

Network Authors (Newman 2004; Lambiotte 2009)

With co-authors networks one can measure the influence of one author, or group of authors (named communities) in a research front

How could we measure the degree of codification and abstraction?

Degree CODIFICATION ABSTRACTION* (II): by citations analysis

High A well defined groups of topics over the years. The relations within the topics and subtopics is clear. The research topics and their relations are clearly identifiable

The research topics of a research front have been “absorbed**” by others research fronts. Papers of a research front are highly cited by others research fronts.

Medium

A well defined group of topics over the years that generated subtopics, with a variations over the years

Some papers of a research front are cited by others research fronts.

Low A lot of topics exists without a clear structure of relations within them

The papers of a research front are not cited by others research fronts.

** (Merton concept of “obliteration by incorporation”).”, we can use i.e the half-live of citations, as longer as it is, less abstraction: the concept has not been yet “absorbed” by others (Upham 2010) .

How could we measure the degree of codification and abstraction?

Degree CODIFICATION ABSTRACTION* (I): by topic relations

High A well defined groups of topics over the years. The relations within the topics and subtopics are clear. The research topics and their relations are clearly identifiable

A well defined group of topics over the years. The same topic appears in different thematic clusters. One topic, not related with others at the beginning is related with other subtopics over the years.Can we say that some kind of centrality could work as an indicator of abstraction?

Medium A well defined group of topics over the years that generated subtopics, with a variations over the years

The topics between different thematic clusters have a weak relations, even that, the relation between topics of two thematic clusters exist.

Low A lot of topics exists without a clear structure of relations within them

There’s not relation within subtopics of different thematic clustersAll the topics only appear in an isolated thematic clusters.

*Further analysis on abstraction need to be done based on the relations and proximity between different fields, also the use of specific topics in different or distant thematic journals.

Our proposalWe’ll use, as a first step, the co-word co-occurrence

technique using title and abstract text of ISI records. We do it because it seems the most suitable one to translated latter to CERN databases.

Even though, we could also use some more literature-based techniques to delineate how knowledge is used and transmitted within a research field.

In a second step, we’d want to explore the above mentioned techniques to evaluated the degree of abstraction and codification in a research field using bibliographic information