| title | author | date | output | ||||||
|---|---|---|---|---|---|---|---|---|---|
Landscape Study - General Overview |
Thomas Klebel |
Last changed 2020-07-05 |
|
The general impression is, that the journals most of the time do not have a specific policy regarding the aspect under question, or it was unclear for our reviewers if there was a policy or not or how to interpret it.
There is quite some missing data, which shouldn't be like this. For example the field U30 in excel (table RAW), which has the opr-responses for the journal "Advanced Materials" is missing. It should probably be "Not specified".
The following are all variables, where implicit missings should be checked and converted to explicit ones (as for opr_responses), or fixed (as for publishers).
The question is, whether it is reasonable to split results by publisher. I think it does not make much sense:
- There are not many publishers, that have multiple publications
- Except Springer Nature and Elsevier, most publishers are confined to one or two subject areas. Looking at publishers would misinterprete differences as being due to the publisher when they are due to the subject area.
- The only thing to compare would be thus Springer Nature vs. Elsevier
I don't think this distinction is very interesting, since it masks disciplinary differences, which are more fundamental.
Only 86 out of 171 do have a policy for manuscript transfer after rejection. Due to policies being similar across journals of certain publishers, there are 48 distinct policies in our dataset.
The following table displays stemmed parts of the distinct policies, sorted by propensity.
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The following graph shows the relationship between to most common bigrams (only bigrams that occur at least three times).
Let's look at the co-review policy.
Only 87 out of 171 do have a coreview-policy. Due to policies being similar across journals of certain publishers, there are 45 distinct policies in our dataset.
The following table displays stemmed parts of the distinct policies, sorted by propensity.
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The following graph shows the relationship between to most common bigrams (only bigrams that occur at least three times).
This doesn't look interesting.
This doesn't look interesting.
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Results so far have revealed that in many cases policies are unclear. But in which ways are policies related to each other? Do journals that allow co-review also allow preprints? Is there a gradient between journals that are pioneers in regard to open science, and others that lag behind? Or are there certain groups of journals, open in one area, reluctant in the second and maybe unclear in the third?
To answer these question, we employ Multiple Correspondence Analysis (MCA). The technique allows us to explore the different policies jointly (Greenacre & Blasius 2006: 27) and thus paint a landscape of open science practices among journals.
To facilitate interpration of the figures, variables had to be recoded. Categories with low counts were merged. Where feasible, we focused on whether certain policies were clear or not, thus omitting the subtle differences within the policies (for example whether journals allowed citations of preprints was simplified for whether the policy was clear (references allowed in text, reference list or not allowed) versus unclear (unsure about policy, no policy and other)).
It should be noted that the procedure is strictly exploratory. We are exploring, not testing any hypothesis.
##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.084830 48.1 48.1 ****************
## 2 0.018979 10.8 58.9 ****
## 3 0.017009 9.6 68.5 ***
## 4 0.006125 3.5 72.0 *
## 5 0.003927 2.2 74.2 *
## 6 0.001318 0.7 75.0
## 7 5.4e-050 0.0 75.0
## -------- -----
## Total: 0.176313
##
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | pr_type_clean:pr_type-Double blind | 54 463 46 | -269 459 47 | -26 4 2 |
## 2 | pr_type_clean:pr_type-Other | 21 758 75 | 967 709 235 | 255 49 73 |
## 3 | pr_type_clean:pr_type-Single blind | 60 524 44 | 93 67 6 | -242 457 187 |
## 4 | pr_type_clean:pr_type-Unsure | 64 611 41 | -181 332 25 | 166 279 92 |
## 5 | coreview_email:coreview-No | 5 167 53 | 194 53 2 | -286 114 20 |
## 6 | coreview_email:coreview-Unsure | 153 502 15 | -50 132 5 | 84 370 57 |
## 7 | coreview_email:coreview-Yes | 43 443 50 | 158 114 13 | -269 329 162 |
## 8 | opr_indenties_author_clean:reviewer_identities-Conditional | 7 248 59 | -98 8 1 | -552 240 114 |
## 9 | opr_indenties_author_clean:reviewer_identities-No | 37 347 49 | 28 4 0 | -260 343 131 |
## 10 | opr_indenties_author_clean:reviewer_identities-Not specified | 118 767 31 | -228 632 72 | 105 135 69 |
## 11 | opr_indenties_author_clean:reviewer_identities-Optional | 38 800 66 | 704 799 221 | 27 1 1 |
## 12 | preprint_version_clean:preprint_version-No policy | 45 213 44 | -118 165 7 | -63 47 10 |
## 13 | preprint_version_clean:preprint_version-None | 11 260 53 | -332 260 14 | -13 0 0 |
## 14 | preprint_version_clean:preprint_version-Other | 6 169 54 | -322 139 7 | 150 30 7 |
## 15 | preprint_version_clean:preprint_version-Unsure | 28 526 49 | -308 526 32 | 10 1 0 |
## 16 | preprint_version_clean:preprint_version-Yes | 110 841 27 | 177 834 41 | 17 7 2 |
## 17 | preprint_citation_clean:preprint_citation-No | 4 89 55 | -228 39 2 | 261 51 13 |
## 18 | preprint_citation_clean:preprint_citation-Not specified | 115 689 27 | -176 688 42 | -5 1 0 |
## 19 | preprint_citation_clean:preprint_citation-Other | 5 385 52 | -296 201 5 | 283 185 20 |
## 20 | preprint_citation_clean:preprint_citation-Unsure | 30 349 50 | -216 242 16 | -144 107 32 |
## 21 | preprint_citation_clean:preprint_citation-Yes | 47 857 60 | 610 850 207 | 55 7 8 |
##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.065420 47.6 47.6 *****************
## 2 0.014348 10.4 58.0 ****
## 3 0.008288 6.0 64.1 **
## 4 0.006491 4.7 68.8 **
## 5 0.003326 2.4 71.2 *
## 6 0.000656 0.5 71.7
## -------- -----
## Total: 0.137454
##
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | pr_type_clean:pr_type-Double blind | 62 412 59 | 234 378 51 | -70 34 21 |
## 2 | pr_type_clean:pr_type-Other | 29 716 84 | -853 716 320 | 17 0 1 |
## 3 | pr_type_clean:pr_type-Single blind | 81 71 51 | -33 16 1 | -61 55 21 |
## 4 | pr_type_clean:pr_type-Unsure | 79 513 52 | 164 349 32 | 112 164 69 |
## 5 | coreview_email:coreview-No | 5 33 69 | 114 21 1 | -87 12 3 |
## 6 | coreview_email:coreview-Unsure | 190 439 18 | -17 23 1 | 74 416 72 |
## 7 | coreview_email:coreview-Yes | 55 444 60 | 50 17 2 | -246 427 233 |
## 8 | preprint_version_clean:preprint_version-No policy | 51 715 59 | 269 616 57 | -108 99 42 |
## 9 | preprint_version_clean:preprint_version-None | 16 471 70 | 370 317 33 | 257 154 73 |
## 10 | preprint_version_clean:preprint_version-Other | 7 464 72 | 146 22 2 | 650 441 193 |
## 11 | preprint_version_clean:preprint_version-Unsure | 33 582 63 | 256 457 33 | -133 124 41 |
## 12 | preprint_version_clean:preprint_version-Yes | 144 903 34 | -201 900 89 | 11 3 1 |
## 13 | preprint_citation_clean:preprint_citation-No | 4 403 73 | -91 5 0 | 819 398 184 |
## 14 | preprint_citation_clean:preprint_citation-Not specified | 144 758 35 | 198 750 87 | -20 7 4 |
## 15 | preprint_citation_clean:preprint_citation-Other | 5 173 69 | 167 55 2 | 244 118 22 |
## 16 | preprint_citation_clean:preprint_citation-Unsure | 35 269 61 | 127 226 9 | 55 43 8 |
## 17 | preprint_citation_clean:preprint_citation-Yes | 62 750 72 | -546 741 280 | -59 9 15 |
## 18 | (*)area:Business, Economics & Management | <NA> 666 <NA> | 476 609 <NA> | 145 56 <NA> |
## 19 | (*)area:Chemical & Materials Sciences | <NA> 680 <NA> | -253 234 <NA> | 350 446 <NA> |
## 20 | (*)area:Engineering & Computer Science | <NA> 597 <NA> | -128 240 <NA> | 156 357 <NA> |
## 21 | (*)area:Health & Medical Sciences | <NA> 13 <NA> | 34 10 <NA> | -18 3 <NA> |
## 22 | (*)area:Humanities, Literature & Arts | <NA> 162 <NA> | 251 126 <NA> | -135 36 <NA> |
## 23 | (*)area:Life Sciences & Earth Sciences | <NA> 765 <NA> | -510 679 <NA> | -181 85 <NA> |
## 24 | (*)area:Physics & Mathematics | <NA> 273 <NA> | -118 173 <NA> | -90 100 <NA> |
## 25 | (*)area:Social Sciences | <NA> 246 <NA> | 285 151 <NA> | -226 95 <NA> |
This looks intelligble, but lets try one more time with this crude variable
##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.094812 50.8 50.8 *****************
## 2 0.020753 11.1 61.9 ****
## 3 0.014912 8.0 69.8 ***
## 4 0.006140 3.3 73.1 *
## 5 0.003838 2.1 75.2 *
## 6 0.001308 0.7 75.9
## 7 7e-05000 0.0 75.9
## -------- -----
## Total: 0.186803
##
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | pr_type_clean:pr_type-Double blind | 49 451 48 | -296 442 45 | -43 9 4 |
## 2 | pr_type_clean:pr_type-Other | 23 770 76 | 988 727 237 | 240 43 64 |
## 3 | pr_type_clean:pr_type-Single blind | 65 482 42 | 78 50 4 | -229 432 164 |
## 4 | pr_type_clean:pr_type-Unsure | 63 659 42 | -211 378 30 | 182 281 100 |
## 5 | coreview_email:coreview-No | 4 71 52 | 142 32 1 | -156 39 5 |
## 6 | coreview_email:coreview-Unsure | 152 524 15 | -41 90 3 | 90 434 59 |
## 7 | coreview_email:coreview-Yes | 44 501 49 | 127 79 8 | -295 422 184 |
## 8 | opr_indenties_author_clean:reviewer_identities-Conditional | 9 243 58 | -103 11 1 | -481 233 105 |
## 9 | opr_indenties_author_clean:reviewer_identities-No | 34 424 49 | -3 0 0 | -300 424 146 |
## 10 | opr_indenties_author_clean:reviewer_identities-Not specified | 116 785 32 | -240 640 71 | 114 145 73 |
## 11 | opr_indenties_author_clean:reviewer_identities-Optional | 41 819 66 | 710 817 217 | 32 2 2 |
## 12 | preprint_version_clean:preprint_version-No policy | 41 297 45 | -167 263 12 | -59 33 7 |
## 13 | preprint_version_clean:preprint_version-None | 13 501 53 | -403 389 22 | 216 112 28 |
## 14 | preprint_version_clean:preprint_version-Other | 5 161 54 | -339 133 6 | 153 27 6 |
## 15 | preprint_version_clean:preprint_version-Unsure | 26 530 49 | -321 517 28 | -51 13 3 |
## 16 | preprint_version_clean:preprint_version-Yes | 115 889 26 | 192 889 45 | 2 0 0 |
## 17 | preprint_citation_clean:preprint_citation-No | 3 93 54 | -231 37 2 | 285 56 12 |
## 18 | preprint_citation_clean:preprint_citation-Not specified | 115 751 27 | -194 748 46 | -13 3 1 |
## 19 | preprint_citation_clean:preprint_citation-Other | 4 364 52 | -314 212 4 | 265 151 14 |
## 20 | preprint_citation_clean:preprint_citation-Unsure | 28 337 49 | -228 274 16 | -110 63 16 |
## 21 | preprint_citation_clean:preprint_citation-Yes | 49 862 61 | 626 857 203 | 52 6 6 |
## 22 | (*)area:Business, Economics & Management | <NA> 790 <NA> | -509 693 <NA> | 190 96 <NA> |
## 23 | (*)area:Chemical & Materials Sciences | <NA> 88 <NA> | 184 85 <NA> | 32 3 <NA> |
## 24 | (*)area:Engineering & Computer Science | <NA> 210 <NA> | 42 30 <NA> | 102 180 <NA> |
## 25 | (*)area:Health & Medical Sciences | <NA> 61 <NA> | 49 25 <NA> | -59 36 <NA> |
## 26 | (*)area:Humanities, Literature & Arts | <NA> 199 <NA> | -304 197 <NA> | 34 2 <NA> |
## 27 | (*)area:Life Sciences & Earth Sciences | <NA> 848 <NA> | 642 848 <NA> | -14 0 <NA> |
## 28 | (*)area:Physics & Mathematics | <NA> 448 <NA> | 140 163 <NA> | -185 284 <NA> |
## 29 | (*)area:Social Sciences | <NA> 261 <NA> | -365 256 <NA> | -50 5 <NA> |
## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: .
## X-squared = 259.28, df = 105, p-value = 4.713e-15
## Warning in stats::chisq.test(tab, correct = FALSE): Chi-squared approximation may be incorrect
## [1] 0.2201843
First axis is distinction between SSH and sciences. Second axis is more difficult in terms of what it means. It also has not that much inertia, thus being not very influential
##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.202431 59.6 59.6 ***************
## 2 0.048870 14.4 74.0 ****
## 3 0.035233 10.4 84.4 ***
## 4 0.024852 7.3 91.8 **
## 5 0.016925 5.0 96.7 *
## 6 0.008446 2.5 99.2 *
## 7 0.002610 0.8 100.0
## -------- -----
## Total: 0.339368 100.0
##
##
## Rows:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | crvwHsn | 187 877 17 | -94 282 8 | -137 596 71 |
## 2 | crvwHsp | 63 877 51 | 280 282 24 | 407 596 213 |
## 3 | prprnt_vrsnNp | 51 606 98 | -473 345 56 | 411 260 176 |
## 4 | prprnt_vrsnNn | 16 980 49 | 341 111 9 | -956 869 294 |
## 5 | prprnt_vO | 7 272 16 | -158 31 1 | -442 241 26 |
## 6 | prprnt_vU | 33 283 44 | -359 282 21 | 26 2 0 |
## 7 | prprnt_vY | 144 682 30 | 219 671 34 | -27 10 2 |
## 8 | preprnt_cttnN | 4 319 23 | 667 224 9 | -434 95 15 |
## 9 | prprnt_cttnNt | 144 391 30 | -164 388 19 | 15 3 1 |
## 10 | prprnt_cO | 5 307 16 | 265 69 2 | 491 237 26 |
## 11 | prprnt_cU | 35 339 35 | -193 110 7 | -278 229 56 |
## 12 | prprnt_cY | 62 595 60 | 431 558 56 | 110 36 15 |
## 13 | pr_D | 62 974 338 | -1347 974 551 | 14 0 0 |
## 14 | pr_tO | 29 461 44 | 432 358 26 | 230 102 31 |
## 15 | pr_S | 81 877 103 | 606 857 147 | 93 20 14 |
## 16 | pr_tU | 79 539 47 | 270 359 28 | -191 179 59 |
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | BsEM | 105 760 123 | -519 673 139 | -186 86 74 |
## 2 | ChMS | 110 825 121 | 381 390 79 | -402 435 364 |
## 3 | EnCS | 126 728 49 | 192 280 23 | -242 447 151 |
## 4 | HlMS | 225 486 97 | 260 464 75 | 57 23 15 |
## 5 | HmLA | 105 818 189 | -697 792 251 | 124 25 33 |
## 6 | LSES | 115 700 128 | 430 493 105 | 279 207 183 |
## 7 | PhyM | 105 628 86 | 306 338 48 | 284 290 172 |
## 8 | SclS | 110 802 209 | -716 797 279 | 58 5 8 |
##
## Principal inertias (eigenvalues):
## 1 2 3 4 5 6 7
## Value 0.202431 0.04887 0.035233 0.024852 0.016925 0.008446 0.00261
## Percentage 59.65% 14.4% 10.38% 7.32% 4.99% 2.49% 0.77%
##
##
## Rows:
## coreview-Has no policy coreview-Has policy preprint_version-No policy preprint_version-None preprint_version-Other preprint_version-Unsure
## Mass 0.187173 0.062827 0.051047 0.015707 0.006545 0.032723
## ChiDist 0.176868 0.526919 0.805284 1.025120 0.899974 0.676312
## Inertia 0.005855 0.017444 0.033103 0.016506 0.005301 0.014967
## Dim. 1 -0.208662 0.621639 -1.051879 0.757799 -0.352191 -0.797899
## Dim. 2 -0.617497 1.839628 1.858271 -4.323368 -1.997743 0.119226
## preprint_version-Yes preprint_citation-No preprint_citation-Not specified preprint_citation-Other preprint_citation-Unsure preprint_citation-Yes
## Mass 0.143979 0.003927 0.143979 0.005236 0.035340 0.061518
## ChiDist 0.267798 1.408809 0.264054 1.006966 0.582205 0.576274
## Inertia 0.010326 0.007793 0.010039 0.005309 0.011979 0.020430
## Dim. 1 0.487619 1.483073 -0.365443 0.589690 -0.429116 0.956954
## Dim. 2 -0.123492 -1.963233 0.069211 2.219163 -1.259100 0.497778
## pr_type-Double blind pr_type-Other pr_type-Single blind pr_type-Unsure
## Mass 0.061518 0.028796 0.081152 0.078534
## ChiDist 1.364655 0.720790 0.654872 0.451213
## Inertia 0.114565 0.014961 0.034803 0.015989
## Dim. 1 -2.993924 0.959127 1.347637 0.601002
## Dim. 2 0.062737 1.042317 0.419260 -0.864563
##
##
## Columns:
## Business, Economics & Management Chemical & Materials Sciences Engineering & Computer Science Health & Medical Sciences
## Mass 0.104712 0.109948 0.125654 0.225131
## ChiDist 0.632291 0.609887 0.361956 0.381467
## Inertia 0.041863 0.040896 0.016462 0.032760
## Dim. 1 -1.153188 0.846897 0.425946 0.577454
## Dim. 2 -0.840234 -1.819903 -1.095257 0.258944
## Humanities, Literature & Arts Life Sciences & Earth Sciences Physics & Mathematics Social Sciences
## Mass 0.104712 0.115183 0.104712 0.109948
## ChiDist 0.783012 0.612972 0.526766 0.802754
## Inertia 0.064200 0.043278 0.029056 0.070852
## Dim. 1 -1.549271 0.956480 0.680514 -1.592465
## Dim. 2 0.561214 1.260950 1.282670 0.264554
Axes are mainly determined by peer review policy. Maybe remove this variable to bring out mor subtle and potentialy more meaningful answers regarding the key topics (that SSH have double blind and Sciences have single blind seems to be well established)
##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.083386 39.3 39.3 **********
## 2 0.057864 27.3 66.6 *******
## 3 0.033091 15.6 82.2 ****
## 4 0.015940 7.5 89.7 **
## 5 0.011894 5.6 95.3 *
## 6 0.007877 3.7 99.0 *
## 7 0.002017 1.0 100.0
## -------- -----
## Total: 0.212068 100.0
##
##
## Rows:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | crvwHsn | 250 853 37 | -39 49 5 | 159 804 108 |
## 2 | crvwHsp | 84 853 110 | 117 49 14 | -472 804 323 |
## 3 | prprnt_vrsnNp | 68 807 208 | -687 727 385 | -229 81 61 |
## 4 | prprnt_vrsnNn | 21 842 104 | 471 211 56 | 814 631 240 |
## 5 | prprnt_vO | 9 273 33 | 57 4 0 | 467 269 33 |
## 6 | prprnt_vU | 44 199 94 | -297 193 46 | 53 6 2 |
## 7 | prprnt_vY | 192 944 65 | 257 921 152 | -41 23 6 |
## 8 | preprnt_cttnN | 5 121 49 | 401 81 10 | 282 40 7 |
## 9 | prprnt_cttnNt | 192 573 63 | -199 569 91 | 18 5 1 |
## 10 | prprnt_cO | 7 345 33 | -64 4 0 | -588 341 42 |
## 11 | prprnt_cU | 47 410 75 | -79 18 4 | 364 391 108 |
## 12 | prprnt_cY | 82 872 128 | 491 727 237 | -220 146 69 |
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | BsEM | 105 800 119 | -373 577 175 | 232 223 98 |
## 2 | ChMS | 110 876 199 | 420 461 233 | 399 415 302 |
## 3 | EnCS | 126 770 85 | 269 506 109 | 194 264 82 |
## 4 | HlMS | 225 153 76 | -37 19 4 | -98 134 37 |
## 5 | HmLA | 105 588 166 | -444 587 248 | 11 0 0 |
## 6 | LSES | 115 915 158 | 344 406 163 | -384 509 294 |
## 7 | PhyM | 105 478 103 | -8 0 0 | -316 478 181 |
## 8 | SclS | 110 299 94 | -227 284 68 | 52 15 5 |
There is not much variation left to explain here, somehow.
##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.246042 65.0 65.0 ****************
## 2 0.059231 15.7 80.7 ****
## 3 0.034367 9.1 89.8 **
## 4 0.024482 6.5 96.2 **
## 5 0.009633 2.5 98.8 *
## 6 0.003869 1.0 99.8
## 7 8e-04000 0.2 100.0
## -------- -----
## Total: 0.378424 100.0
##
##
## Rows:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | pr_D | 82 989 404 | -1357 988 614 | 38 1 2 |
## 2 | pr_tO | 38 352 53 | 395 300 24 | -163 51 17 |
## 3 | pr_S | 108 904 123 | 606 857 162 | -142 47 37 |
## 4 | pr_tU | 105 571 56 | 292 417 36 | 177 153 55 |
## 5 | crvwHsn | 250 867 21 | -92 269 9 | 137 598 79 |
## 6 | crvwHsp | 84 867 61 | 273 269 25 | -407 598 235 |
## 7 | prprnt_vrsnNp | 68 672 117 | -470 341 61 | -464 331 247 |
## 8 | prprnt_vrsnNn | 21 926 58 | 373 132 12 | 913 794 295 |
## 9 | prp_O | 9 255 19 | -152 28 1 | 429 227 27 |
## 10 | prp_U | 44 266 53 | -348 265 22 | 7 0 0 |
## 11 | pr_Y | 192 654 36 | 212 627 35 | 44 27 6 |
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | BsEM | 105 816 112 | -522 676 116 | 237 139 99 |
## 2 | ChMS | 110 917 100 | 402 468 72 | 393 449 287 |
## 3 | EnCS | 126 916 53 | 230 334 27 | 304 582 196 |
## 4 | HlMS | 225 750 90 | 322 687 95 | -98 63 36 |
## 5 | HmLA | 105 838 216 | -793 805 268 | -159 32 45 |
## 6 | LSES | 115 618 104 | 410 489 79 | -210 129 86 |
## 7 | PhyM | 105 799 86 | 329 349 46 | -374 450 247 |
## 8 | SclS | 110 810 239 | -815 808 297 | -39 2 3 |
## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: .
## X-squared = 155.56, df = 35, p-value < 2.2e-16