a bibliometric approach to international scientific migration henk f. moed, mhamed aisati, andrew...
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A bibliometric approach to international scientific migration
Henk F. Moed, M’hamed Aisati, Andrew Plume and Gali Halevi
Elsevier (Netherlands, UK, USA)
Contents
• Introduction: Migration and co-authorship• The model• Technical aspects • Study countries and approaches• Results: Global patterns• Publication (Scopus) vs. Survey (OECD) data• Accuracy / robustness of migration indicators• Conclusions
Introduction:Migration vs. co-authorship
Which country has these main collaborators?
Main collaborators
Argentina
USA
Portugal
France
Chile
Brazil
Which country has these main collaborators?
Main collaborators
Thailand
India
Singapore
Iran
UK
Malaysia
Which country has these main collaborators?
Main collaborators
France
Hungary
Germany
Italy
Bulgaria
Romania
Which country has these main collaborators?
Main collaborators
UK
China
USA
Nigeria
Australia
Netherlands
South Africa
The model
International migration vs. co-authorship
Relationship Definition Comment
International co-authorship
Authors from institutions located in different countries jointly publish a paper
Country relates to where authors work, NOT to their nationality International
migrationA scientific author moves from one country to another
The model - 1Theoretical concept; Interpretation
Bibliometric construct / feature
Researcher Author id
Research active (in a year) Publishing (in a year)
Currently active Publishing in 2011
Starting a scientific career during years T1-T2
First publication during T1-T2
“Young” in 2011 First publication year > 2000
Moving from country A to B Publishing author’s “work” country changes consecutively from A to B Transients are
deleted
The Model – 2
Master PhD student Post doc Senior
Master degree
PhD degree
Start senior career
T
First publi-cation
2nd p 3rd p 4th p
B B B B B A A
A A B B B A A
A A A A A B A
2
1
3
Technical aspects
Technical aspects (in Scopus)
1. Author-affiliation linking
2. Author profiling
Author-affiliation links
Is Italian science declining? RESPOL, 2011Cinzia Daraio (a) and Henk F. Moed (b)
(a) Department of Management, Univ Bologna, Via U. Terracini, 28, 40131 Bologna, Italy
(b)Centre for Science and Technology Studies (CWTS), Leiden University, The Netherlands
Author-affiliation link records
Author Year Affiliation
...... ...... ......
Daraio, Cinzia 2011 Univ Bologna, ..., Italy
Moed, Henk F. 2011 Univ Leiden,..., Netherlands
---- .... ....
Author profiling in Scopus
• Assigns to each author a unique number• Groups each author’s documents....• ... based on similarity in affiliation,
publication history, subject and co-authors• Aims at high precision ( lower recall)• New algorithm implemented in April 2011• Author feedback system in place
Categorization of authors in year T
TYPE T-4 T-3 T-2 T-1 T T+1 T+2 T+3 T+4
Continuant X X X
Newcomer X X
Mover X X
Transient X
[Active but not publishing]
X X
# Publishing authors per year and type (UK)
CONTINUANTS
MOVERS
TOTAL
TRANSIENTS
NEWCOMERS
Decline in # Newcomers
2009 Cohort can be followed for one year only
Important distinction
Use of author ID data to assess an individual
vs
Statistical analysis of patterns in large datasets
X
Study countries and approaches
Countries with the largest increase in publication output during 2000-2010 (Scopus)
> 10 %: Selected in
current study
Study countries (10 fast growers + 7 “big” countries)
D8 EU BRIC Other
Egypt Romania Brazil Thailand
Iran Portugal China
Malaysia India Australia
Pakistan Germany Japan
Italy USA
Netherlands
UK
Two complementary approaches
Feature Synchronous Asynchroneous
Publication years analyzed
Fixed [2011]
Variable [2001-2011]
Starting years of authors’ careers
Variable [2001-2010]
Fixed [ 2001-2003]
Results: Synchronous approach
Study set: % Young authors currently active in a study country Question: How many of them had stays abroad?
Results: Asynchroneous approach
Study set: authors starting their career in a study countryQuestion: How many moved abroad, how many returned?
Study set: Young authors starting their career in a study countryQuestion: How many move abroad, how many return?
Post Docs returning to their home country?
Post-Docs not returning to their country? Or PhD students returning after attaining their PhD?
Results: Publication (Scopus) data
vs. Survey (OECD) data
Inconsistencies in OECD data on # FTE Res?
CountryOECD
# FTE Res All
(2007)
OECD# FTE Res Gov+HE
(2007)
Scopus# Authors
(2007)
Ratio # authors /# FTE Res
All
Ratio # authors /# FTE Res Gov+HE
UK 254,600 159,100 154,600 0.61 0.97
ITA 93,000 56,200 113,100 1.22 2.01Differences in
ratios between ITA and UK are
almost a factor 2!
Inconsistencies in data on # FTE Res?
Country# FTE Res
All(2007)
# FTE Res Gov+HE
(2007)
# Authors Scopus
(2007)
Ratio # authors /# FTE Res
All
Ratio # authors /# FTE Res Gov+HE
DEU 290,800 116,600 150,400 0.52 1.29
UK 254,600 159,100 154,600 0.61 0.97
ITA 93,000 56,200 113,100 1.22 2.01
NLD 49,700 23,800 46,300 0.93 1.95
Differences in ratios between {ITA, NLD} and
{DEU, UK} almost a factor 2
Results: Migration vs. Co-authorship
Migration and co-authorship patterns are statistically different
N=694Mean=0.75STD=0.49Skewness=1.59
Analyzed later
Map of countries with Ratio migration/collaboration > 1.2
Study country = TO COUNTRY
FROM Country
No. Co-authored papers
No. Migrating authors
Ratio % Migration / % Co-authorship
PAK IND 276 118 3.6PRT BRA 1,971 423 3.4IND PAK 276 96 3.3NLD IRN 492 80 3.1USA IND 12,013 3,307 2.8NLD PAK 145 21 2.8CHN TWN 3,979 1,048 2.6USA IRN 3,039 780 2.6BRA PRT 1,971 352 2.4MYS NGA 122 31 2.1
Language similarity drives migration stronger than it drives co-authorship
Political tensions affect migration less than they affect co-authorship
Tentative error rates in migration indicators
Indicator Set N Mean variation
STD Skew-ness
Rough error rate
# or % authors per country
All 17 11.1 2.9 -0.25 10 %
# or % migrating authors per country pair
Top 100All
100
650
2.1
11.1
1.2
28.8
1.4
7.0
3 %>10%
Ratio migration/Collaboration per country pair
Top 100All
100
650
2.8
10.6
2.4
27.6
2.4
7.4
5 % >10%
Variation = 100*(max-min)/
(2*mean)
Conclusions
• Migration analysis generates new insights into the global scientific network
• Relative indicators based on large numbers are insensitive to errors in author profiles.
Thank you for your attention
PART II Robustness / accuracy of migration indicators
Case study: Data sample analysed
• 100 author-ids in chemistry randomly selected from Scopus
• Search for other author-ids that relate to the same authors as those represented in the sample
• Precision of the 100 author-ids themselves was not examined
• Emphasis on recall rather than precision
Author Countries
Asian countries (China, Japan and Korea) are over-represented (n=34)
CHNUSA
JPNDEUKOR
Findings – 1
• 27% of the researchers did have additional author ID's
• For the 27 % researchers that did have additional ID's, in total 51 additional author IDs were found
• Half of the additional author IDs had 1 paper only; and 75 % at most 2
100 sample authors are linked with 151 author-ids
# Author-ids with 1 paper = 2 x # authors with 1 paper
# Author-ids with 2 papers = 1.6 x # authors with 2 paper
# Author-ids with >=20 papers is 1.1 x # authors with >=20 papers
Implications for the migration study
Homonyms Synonyms
Definition/ Examples
One author id relates to different persons (common names)
A researcher has more than one author id (split identities)
Tendencies The more common a name, the larger the number of articles
Second identity for author ids with few articles only (typically 1-3)
In analysis Newcomers as from 2001
Transients are not taken into account
Sensitivity Analysis: publication thresholds
1. ALL
2. P > 2
3. P > 3
Sensitivity analysis: publ per year (Ppyr) thresholds
1. All
2. Ppyr < 7.0
3. Ppyr < 5.0
No. Authors moving to a study country
12 3
% Authors moving to a study country
12 3
Ratio % migration/% co-authorship per country pair for author sets 1 and 2
Rank order hardly changes
1
2
Variation in # or % migrating authors per country-pair
Rank No. AuthorsMean
variation STD
1-100 1002 2.08 1.71
101-200 329 3.50 2.91
201-300 130 5.40 6.50
301-400 81 7.94 11.53
401-500 54 8.61 17.25
501-600 22 18.28 34.07
1-650 270 11.14 28.77
Variation = 100*(max-min)/
(2*mean)
+
Tentative error rates in migration indicators
Indicator Set N Mean variation
STD Skew-ness
Rough error rate
# or % authors per country
All 17 11.1 2.9 -0.25 10 %
# or % migrating authors per country pair
Top 100All
100
650
2.1
11.1
1.2
28.8
1.4
7.0
3 %>10%
Ratio migration/Collaboration per country pair
Top 100All
100
650
2.8
10.6
2.4
27.6
2.4
7.4
5 % >10%
Variation = 100*(max-min)/
(2*mean)
Conclusions
• Migration analysis generates new insights into the global scientific network
• Relative indicators based on large numbers are insensitive to errors in author profiles.