Author:
Erik Angelone Department of Modern and Classical Language Studies, Institute for Applied Linguistics, Kent State University, USA

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Abstract

To date, the assessment of student translations has been largely based on configurations of error categories that address some facet of the translation product. Focal points of such product-oriented error annotation include language mechanics (punctuation, grammar, lexis and syntax, for example) and various kinds of transfer errors. In recent years, screen recording technology has opened new doors for empirically informing translation assessment from a more process-oriented perspective (Massey and Ehrensberger-Dow, 2014; Angelone, 2019). Screen recording holds particular promise when tracing errors documented in the product back to potential underlying triggers in the form of processes that co-occur on screen in their presence. Assessor observations made during screen recording analysis can give shape to process-oriented error categories that parallel and complement product-oriented categories. This paper proposes a series of empirically informed, process-oriented error categories that can be used for assessing translations in contexts where screen recordings are applied as a diagnostic tool. The categories are based on lexical and semantic patterns derived from a corpus-based analysis of think-aloud protocols documenting articulations made by assessors when commenting on errors made in student translations while watching screen recordings of their work. It is hoped that these process-oriented error categories will contribute to a more robust means by which to assess and classify errors in translation.

  • Angelone, E. (2010). Uncertainty, uncertainty management, and metacognitive problem solving in the translation task. In G.M. Shreve , & E. Angelone (Eds.), Translation and cognition (pp. 1740). Amsterdam/Philadelphia: John Benjamins.

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  • Angelone, E. (2015). The impact of process protocol self-analysis on errors in the translation product. In M. Ehrensberger-Dow , B. Englund Dimitrova , S. Hubscher-Davidson , & U. Norberg (Eds.), Describing cognitive processes in translation (pp. 195214). Amsterdam/Philadelphia: John Benjamins.

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  • Angelone, E. (2019). Process-oriented assessment of problems and errors in translation: Expanding horizons through screen recording. In S. Vandepitte , E. Huertas Barros , & E. Iglesias Fernandez (Eds.), Quality assurance and assessment practices in translation and interpreting (pp. 179199). Hershey, PA: IGI Global.

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  • Angelone, E. (2020). The impact of screen recording as a diagnostic process protocol on inter-rater consistency in translation assessment. The Journal of Specialized Translation, 34, 3250.

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  • Angelone, E. , & Shreve, G.M. (2011). Uncertainty management, metacognitive bundling in problem-solving, and translation quality. In S. O’Brien (Ed.), Cognitive explorations of translation (pp. 108130). London/New York: Continuum.

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  • Brunette, L. (2000). Towards a terminology for translation quality assessment: A comparison of TQA practices. The Translator, 6(2), 169182.

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  • Doherty, S. (2017). Issues in human and automatic translation quality assessment. In D. Kenney (Ed.), Human issues in translation technology. London: Routledge. 131148.

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  • Galán-Mañas, A. , & Hurtado Albir, A. (2015). Competence assessment procedures in translator training. The Interpreter and Translator Trainer, 9(1), 6382.

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  • Huertas Barros, E. , & Vine, J. (Eds.). (2019). New perspectives on assessment in translator education. London: Routledge.

  • Kornacki, M. (2019). The application of eye-tracking in translator training. In P. Pietrzak (Ed.), inTRAlinea. Special issue: New insights into translator training. Retrieved December 20, 2020 from http://www.intralinea.org/specials/article/2421.

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  • Mariana, V. , Cox, T. , & Melby, A. (2015). The Multidimensional Quality Metrics (MQM) framework: A new framework for translation quality assessment. The Journal of Specialised Translation, 23, 137161.

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  • Massey, G. , & Ehrensberger-Dow, M. (2011). Investigating information literacy: A growing priority in translation studies. Across Languages and Cultures, 12(2), 193211.

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  • Massey, G. , & Ehrensberger-Dow, M. (2014). Looking beyond the text: The usefulness of translation process data. In J. Jan Engberg , C. Heine , & D. Knorr (Eds.), Methods in writing process research (pp. 8198). Frankfurt: Peter Lang.

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  • Mellinger, C. , & Shreve, G.M. (2016). Match evaluation and over-editing in a translation memory environment. In Martín, Ricardo Muñoz (Ed.), Re-embedding translation process research (pp. 131148). Amsterdam/Philadelphia: John Benjamins.

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  • Moorkens, J. , Castilho, S. , Doherty, S. , & Gaspari, F. (Eds.). (2018). Translation quality assessment: From principles to practice. Berlin: Springer.

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  • Mossop, B. (2001). Revising and editing for translators. Manchester: St. Jerome.

  • Schäffer, M. , Nitzke, J. , Tardel, A. , Oster, K. , Gutermuth, S. , & Hansen-Schirra, S. (2019). Eye-tracking revision processes of translation students and professional translators. Perspectives: Studies in Translation Theory and Practice, 27(4), 589603.

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  • Shreve, G.M. , Angelone, E. , & Lacruz, I. (2014). Efficacy of screen recording in the other-revision of translations: Episodic memory and event models. MonTi: Monografías de traducción e interpretación, 1, 225245.

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  • TAUS (2016). TAUS quality dashboard. Retrieved November 18, 2020 from https://www.taus.net/data/dqf.

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  • WoS Arts & Humanities Citation Index
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2022  
Web of Science  
Total Cites
WoS
283
Journal Impact Factor 0.7
Rank by Impact Factor

Linguistics (Q3)

Impact Factor
without
Journal Self Cites
0.6
5 Year
Impact Factor
1.4
Journal Citation Indicator 0.66
Rank by Journal Citation Indicator

Linguistics (Q3)
Language & Linguistics (Q2)

Scimago  
Scimago
H-index
20
Scimago
Journal Rank
0.796
Scimago Quartile Score

Linguistics and Language 67/1103 (Q1)

Scopus  
Scopus
Cite Score
1.6
Scopus
CIte Score Rank
Language and Linguistics 208/1001 (79th PCTL)
Linguistics and Language 243/1078 (77th PCTL)
Scopus
SNIP
0.868

2021  
Web of Science  
Total Cites
WoS
214
Journal Impact Factor 1,292
Rank by Impact Factor Linguistics 98/194
Impact Factor
without
Journal Self Cites
1,208
5 Year
Impact Factor
1,210
Journal Citation Indicator 0,85
Rank by Journal Citation Indicator Language & Linguistics 108/370
Linguistics 122/274
Scimago  
Scimago
H-index
19
Scimago
Journal Rank
0,994
Scimago Quartile Score Linguistics and Language 67/1103 (Q1)
Scopus  
Scopus
Cite Score
2,5
Scopus
CIte Score Rank
Language and Linguistics 121/968 (Q1, D2)
Linguistics and Language 128/1032 (Q1, D2)
Scopus
SNIP
1,576

2020  
Total Cites
WoS
169
Journal Impact Factor 1,160
Rank by Impact Factor

Linguistics 99/193 (Q3)
Languages & Linguistics 57/205 (Q2)

Impact Factor
without
Journal Self Cites
1,040
5 Year
Impact Factor
1,095
Journal Citation Indicator 1,01
Rank by Journal Citation Indicator

Linguistics 107/259 (Q2)
Language & Linguistics 94/356 (Q2)

Citable
Items
12
Total
Articles
12
Total
Reviews
0
Scimago
H-index
14
Scimago
Journal Rank
1,257
Scimago Quartile Score

Language and Linguistics Q1
Linguistics and Language Q1

Scopus
Cite Score
93/50=1,9

Scopus
Cite Score Rank

Language and Linguistics 130/879 (Q1)
Linguistics and Language 147/935 (Q1)
Scopus
SNIP
1,670

2019  
Total Cites
WoS
91
Impact Factor 0,360
Impact Factor
without
Journal Self Cites
0,320
5 Year
Impact Factor
0,500
Immediacy
Index
0,083
Citable
Items
12
Total
Articles
12
Total
Reviews
0
Cited
Half-Life
n/a
Citing
Half-Life
12,7
Eigenfactor
Score
0,00018
Article Influence
Score
0,234
% Articles
in
Citable Items
100,00
Normalized
Eigenfactor
0,02306
Average
IF
Percentile
20,053 (Q1)
Scimago
H-index
13
Scimago
Journal Rank
0,648
Scopus
Scite Score
94/51=1,8
Scopus
Scite Score Rank
Language and Linguistics 120/830 (Q1)
Linguistics and Language 135/884 (Q1)
Scopus
SNIP
1.357

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