The Impact Of Open-Acess Self-Archiving Mandate On Citation Advantage



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Conclusion


Only about 15% of articles have been self-archived. To reach 100% OA, self-archiving needs to be mandated by researchers' employers and funders, as they are now increasingly beginning to do. By mandating self-archiving, we enhance the impact it has been shown OA will generate (Harnad & Brody 2004, Hajjem and al, 2005) and we develop assessing, crediting and rewarding research productivity and progress (Harnad, 2007). This study confirms the OA advantage for non-mandated articles as well as for mandated ones.
A growing number of universities worldwide already have IR. We believe that the adoption of more official university OA self-archiving mandates will certainly help to increase the number of such archives, as well as the number of articles in them. Both universities and funders should indeed mandate immediate deposit of the peer-reviewed final draft, in the author's own university's IR, immediately upon acceptance for publication, without exceptions or opt-outs, for institutional record-keeping purposes.
The motivation for the Open Access movement -- and hence for OA self-archiving by researchers and OA Self-Archiving Mandates by researchers' institutions and funders -- is to maximize research access in order to maximize research uptake, usage, impact, productivity and progress, for the benefit of research, researchers, their institutions and funders, and the tax-paying public that supports them and in whose interests the research is being conducted and published.
Ideas and knowledge derived from publicly-funded research must be made available and accessible for public use, interrogation and scrutiny, as widely, rapidly and effectively as practicable. However, there is no need for any penalties or sanctions for non-compliance with the mandate; the mandate and its own rewards (enhanced research access and impact) will take care of itself, as the experience of Southampton ECS, Minho, QUT and CERN has already demonstrated. Awareness of the OA and mandate advantages would certainly be more powerful than sanctions.
What institutions and funders need to mandate is that, immediately upon acceptance for publication, the author’s final draft must be deposited into the IR. Only the depositing needs to be mandated; setting access privileges to the full-text as either OA or Closed Access can be left up to the author.

References
Brody, T. and Harnad, S. (2004) Comparing the Impact of Open Access (OA) vs. Non-OA Articles in the Same Journals. D-Lib Magazine 10(6).

http://eprints.ecs.soton.ac.uk/10207/
Davis, P.M. (2008) Author-choice open access publishing in the biological and medical literature: a citation analysis. Journal of the American Society for Information Science and Technology (JASIST)
Eysenbach G. (2006) Citation Advantage of Open Access Articles. PLoS Biology 2006;4(5).
Hajjem, C., Harnad, S. and Gingras, Y. (2005) Ten-Year Cross-Disciplinary Comparison of the Growth of Open Access and How it Increases Research Citation Impact. IEEE Data Engineering Bulletin 28(4) pp. 39-47.

http://eprints.ecs.soton.ac.uk/11688/


Harnad, S. (2007) Open Access Scientometrics and the UK Research Assessment Exercise. Proceedings of 11th Annual Meeting of the International Society for Scientometrics and Informetrics 11(1), pp. 27-33, Madrid, Spain. Torres-Salinas, D. and Moed, H. F., Eds. http://eprints.ecs.soton.ac.uk/13804/
Harnad, S. & Brody, T. (2004) Comparing the Impact of Open Access (OA) vs. Non-OA Articles in the Same Journals, D-Lib Magazine 10 (6) June http://www.dlib.org/dlib/june04/harnad/06harnad.html
Kurtz, Michael J., Guenther Eichhorn, Alberto Accomazzi, Carolyn Grant, Markus Demleitner, Edwin Henneken, and Stephen S. Murray. (2004). The effect of use and access on citations. Unpublished paper, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA.
Kurtz, M. & Brody, T. (2006) The impact loss to authors and research, in Jacobs,

Neil, Eds. Open Access: Key Strategic, Technical and Economic Aspects. Chandos Publishing (Oxford) Limited.


Lawrence, S. (2001) Free online availability substantially increases a paper's impact Nature, 31 May 2001 http://www.nature.com/nature/debates/eaccess/

Articles/lawrence.html


Moed, H. F. (2005) Statistical Relationships Between Downloads and Citations at the Level of Individual Documents Within a Single Journal, Journal of the American Society for Information Science and Technology, 56(10): 1088-1097
Odlyzko, A. (2006) The economic costs of toll access, in Jacobs, Neil, Eds. Open Access: Key Strategic, Technical and Economic Aspects. Chandos Publishing (Oxford) Limited.
Swan, A. and Brown, S. (2005) Open access self-archiving: An author study. JISC Technical Report, Key Perspectives, Inc.
SWAN, A. (2006) The culture of Open Access: researchers’ views and responses. In : Jacobs, N., Eds. Open Access: Key Strategic, Technical and Economic Aspects. Oxford : Chandos, chapter 7, p.52-59, 2006. http://eprints.ecs.soton.ac.uk/12428

SWAN, A. and CARR, L (2008). Institutions, their repositories and the Web. Serials Review, 34 (1) 2008. http://eprints.ecs.soton.ac.uk/14965/1/Serials_Review_article.doc



Appendix 1

 Figure 11: Comparisons between articles groups for Southampton

 

 Figure 12: Comparisons between articles groups for Queensland

 



Figure 13: Comparisons between articles groups for Minho 

 



 Figure 14: Comparisons between articles groups for CERN

Appendix 2: Paired Samples Test
 
Appendix 3

Model N.

Dependent V.

Age

IF

Ref_N

Auth_N

Page_N

OA

M

USA

Review

Sci

CERN

South

Minho

Queens

Age*OA

    M_1

Cit_a_0&1-5

1.494

2.229

1.020

1.007

0.993

0.957

 

 

0.627

1.249

0.789

 

 

1.476

1.209

M_2

Cit_a_1-5&5-10

1.490

1.514

1.016

1.002

0.986

1.323

1.889

1.415

0.777

1.475

 

 

 

 

 

M_3

Cit_a_1-5&10-20

1.786

1.776

1.020

1.002

0.992

1.392

1.716

1.406

0.992

1.887
















M_4

Cit_a_1-5&20+

2.439

2.114

1.019

0.999

 

8.953

 

1.860

1.914

3.050

2.306

 

 

 

0.968

Table 3:  The Exp(ß) values for logistic regressions
Bold: p<0.01

Italic: 0.01≤p<0.05


 

Appendix 4 : Multiple regression by IF – Beta values

IF1 (IF < 0,633)



Model N.

Dependent Var.

Age

Ref_N

Auth_N

Page_N

OA

M

USA

Review

Sci

CERN

South

Minho

Queens

Age*OA

M_1

Cit_a_0&1-5

1,537

1,017

1,079

 

 

 

 

 

 

 

 

0,701

 

1,093

M_2

Cit_a_1-5&5-10

1,847

1,013

1,066

 

 

1,881

 

 

 

 

 

 

 

1,059

M_3

Cit_a_1-5&10-20

2,071

1,026

1,054

0,962

1,533

1,902

 

 

 

 

 

 

 

 

M_4

Cit_a_1-5&20+

2,689

1,020

1,087

 

2,406

 

 

4,760

3,214

 

 

 

 

 

IF2 (0,633 <= IF < 1,053)




Model N.

Dependent Var.

Age

Ref_N

Auth_N

Page_N

OA

M

USA

Review

Sci

CERN

South

Minho

Queens

Age*OA

M_1

Cit_a_0&1-5

1,407

1,016

1,028

 

 

1,265

 

0,605

 

0,511

 

 

 

 

M_2

Cit_a_1-5&5-10

1,548

1,012

 

 

1,346

1,963

 

 

 

 

 

 

 

 

M_3

Cit_a_1-5&10-20

1,869

1,018

1,007

 

1,337

1,722

 

 

 

 

 

 

 

 

M_4

Cit_a_1-5&20+

2,117

1,011

 

 

2,322

 

 

3,106

 

 

 

 

 

 

IF3 (1,053 <= IF < 1,743)




Model N.

Dependent Var.

Age

Ref_N

Auth_N

Page_N

OA

M

USA

Review

Sci

CERN

South

Minho

Queens

Age*OA

M_1

Cit_a_0&1-5

1,581

1,012

1,032

 

1,236

 

 

 

 

0,401

 

 

1,856

 

M_2

Cit_a_1-5&5-10

1,540

1,007

1,033

 

 

1,428

1,330

 

 

 

 

 

 

 

M_3

Cit_a_1-5&10-20

1,879

1,013

1,026

 

1,263

 

 

 

 

 

 

 

1,382

 

M_4

Cit_a_1-5&20+

2,305

1,009

1,041

1,026

1,449

1,492

1,791

1,939

 

 

3,734

 

 

 


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