Index
A
- Abusive subtitling
180–182
- Activity theory
104, 201, 219
- Actor-Network theory
101, 199
- Agency, translator
6, 61, 107, 197, 199–200, 234–235
- Amara
22–23, 32, 35, 56, 65, 69–70, 73, 79–80, 84, 135, 143–144, 168, 171, 225
- Amazon Mechanical Turks
13, 83
- Anime
29, 35, 47–48, 136, 181, 185–186, 188, 193, 214–215
- App localization
30, 86, 235, 259
- Audiovisual translation
8, 35, 56–57, 62, 79–80, 84, 179, 186–188, 231
B
- Best practices, crowdsourcing
64, 91–95, 132
- Bourdieu, Pierre
195, 197–199, 202
- Bowker, Lynne
1, 4, 52, 146–147, 151–153, 157–162, 169, 172, 229, 245
- Brabham, Daren
13–18, 20–21, 25–26, 28, 37, 108, 116, 159, 175, 212
- Buzelin, Hélène
41, 103, 172, 195–200
C
- Camara, Lidia
19–20, 30, 80, 94, 132–133, 195–196, 217, 220–221, 223–225, 241
- Chesterman, Andrew
24, 61, 104, 142, 176, 182–185, 195–196, 203–207, 220, 262–263
- Cognitive approaches
142, 166, 172–173, 241, 263
- Cognitive Translation Studies
97–120
- Cognitive Translatology
2, 97, 98, 99–100, 109–113, 117–118, 241–250
- Distributed cognition
98, 101–103, 117, 200
- Embodied cognition
7, 98, 100–101
- Extended cognition
18, 97, 99–100, 103, 106, 120
- Situated cognition
100, 263
- Cognitive surplus
222, 257
- Coherence,
67, 117, 140, 154, 157–158, 162–163, 166–167, 169–170, 175
- Collaborative translation protocols
118–120
- Collective intelligence
12–15, 18, 46, 50, 60, 69–70, 108, 138–139, 166–167
- Common Sense Advisory
45, 55, 92–93, 255, 260
- Community translation
18, 23–28, 54, 241, 255
- Community managers
72, 81, 103, 132–134, 209
- Comparable corpus
75, 152–155, 175
- Content Management Systems63
- Content prioritization
3, 65, 123, 130, 261
- Copyright
49, 209, 213–216
- Corpus-based Translation Studies
2, 154–155, 172, 175–176, 263
- Quality evaluation and corpora
151–153
- Cronin, Michael
1, 4–5, 23, 25, 47, 72, 161, 201, 212, 259–260, 263
- CrowdIn
32, 65–67, 69, 72–73, 78–79, 83, 171–172
- Crowdsourcing typologies
15–17, 31–32
- Crowdsourcing platforms
31, 33, 42, 51, 62, 64, 66, 73, 76–77, 82–86, 134, 163, 171–172, 212, 263
D
- Deliberate practice
6, 110–111, 227, 231–232, 243, 254
- DePalma, Don
2, 30–31, 45, 52, 54–55, 57, 59, 62–64, 72, 78, 85, 91–94, 133–134, 160, 211, 230, 257–258, 260
- Díaz Cintas, Jorge
29, 48–49, 75, 158, 179, 182, 186, 188–192, 213, 215
- Dotsub
56, 65, 69–70, 73, 79, 84, 143–144
- Drugan, Johanna
7–8, 121, 123–125, 130, 143–145, 186, 204, 209–210
- Duolingo
19, 31, 33–34, 84, 154, 168, 217, 219–220, 227
E
- Estellés, Enrique
11–16, 18, 21, 108, 263
- Ethics
8, 49, 61, 181, 185–186, 195–197, 203–213, 263
- MT and translation ethics
207–209
- Experienced non-experts111
- Expertise
6–7, 9, 17, 24, 31, 3–34, 67–68, 97–100, 109–117, 129–130, 163, 170–171, 224, 228, 231, 236, 239, 249, 254, 257
- Routine and adaptative experts111
- Absolute and relative experts
111–112
- Translation expertise
6, 31, 99–100, 110–117, 130, 163, 170, 228, 231, 236, 257
F
- Facebook
18, 21–22, 27, 30, 32, 35, 37–38, 44, 46–47, 52–54, 59, 65–76, 83–84, 102, 108, 116–117, 122, 133–137, 140–141, 146–149, 151, 153–155, 166–167, 170–172, 176–177, 201, 208, 217–220, 224–225, 239–240, 254, 257–258
- Fandubbing
22, 28–29, 183, 189–190
- Fansubbing
2, 4, 28–29, 46–49, 81–82, 154, 179–194, 212–216, 222–224, 234, 260
- Fansubbing and copyright
213–216
- Fansubbing process
81–82, 193
- Feedback
55, 68–69, 73–78, 80, 88, 93–94, 108, 110, 116–118, 132–134, 139, 143–146, 219–220, 222, 227–228, 231–232, 235–241, 248–249, 254
- Feedback classification
236–241
- Fitness for purpose
58, 122, 132
- Free and Open Machine Translation (FOMT)
2–3, 86–87
- Functionalism
18, 137, 140, 147, 149–151, 165, 184, 229, 233
G
- Gambier, Yves
2–6, 11, 17, 22–25, 33, 39, 40–41, 57, 185, 187–190, 195, 201, 212, 231–232, 260–262
- García, Ignacio
2, 24–25, 31, 38, 50, 58, 61, 86–87, 90, 123–128, 130, 161, 228–229, 239, 251, 260–261
- Gengo
7–8, 34, 58, 65, 128–129, 227
- Glosses
180, 186–188, 190–191
- Göpferich, Susanne
9, 109–111, 114–116, 120, 149–151, 158, 165–168, 235–236, 242, 246–248
- Gouadec, Daniel
6, 18–19, 62–64, 70–71, 111, 123, 125–127, 140–142, 228–230, 233, 235–236, 259
H
- Habitus
197–199, 212, 228, 231–232
- Hermans, Theo
12, 61, 182, 184–186, 196–197
- Hurtado Albir, Amparo
33, 117, 130, 137–139, 144, 158, 166, 229, 243, 245, 249, 251
I
- Interdisciplinarity
4–7, 203, 230, 261–262
- Iterative translation models
68–70, 89, 132, 135, 141, 146–151, 172–174, 176–177, 239–240, 248, 254
J
- Jääskeläinen, Rita
97, 108–109, 111, 114–115, 119–120, 162, 228–229, 259
K
- Kanjingo
22–23, 77, 84–85
- Kiraly, Don
109–110, 227, 229–233, 237, 242–244, 246, 248–249, 251, 253–254, 259
- Kiva
16, 18, 21–22, 30–32, 36, 52, 55, 66–67, 73, 76–77, 84, 103, 132–133, 138, 144–145, 171, 176, 235
L
- Language Industry perspectives
62–63, 73–81, 91–95, 129–131, 258–261
- Literary Translation
6, 18–19, 26, 33, 37, 40–41, 127–128, 136, 139, 196, 198–199, 213, 250–251
M
- Machine Translation (MT)
1–3, 5–6, 7–8, 26–27, 29, 31–32, 35–38, 45, 48, 50–51, 59, 61–64-68, 70, 72–74, 77–79, 82–91, 113, 117–118, 121–124, 126–129, 131–132, 134–141, 144–146, 154, 159–160, 163, 176–177, 195–196, 207–209, 211–213, 217, 229–230, 238–239, 240, 252, 258–261
- Statistical MT
3, 50, 66–68, 78, 85, 88–89, 146, 207–208
- Google Translate
2, 27, 36, 66, 78, 85–88, 118, 126, 146, 238–239
- Microsoft Bing
66, 78–79, 85–87, 118, 136–137, 207–208, 238–239
- Manga
29, 35, 47–48, 214, 225, 244, 257
- Many eyes principle
132, 135–136, 138–139
- Massidda, Serenella
4, 48–49, 79, 81, 154, 179–181, 185, 187–193, 212–214, 260–261
- MateCat
42, 82, 85–88, 212–213, 258
- MNH-TT
30–31, 76–77, 83–84, 134, 227, 234–235, 252
- Mobile translation
29, 58–59, 70, 85, 120, 230, 257, 259
- Motivation
8, 17, 25, 33, 36, 48, 53–54, 58, 64–65, 67–69, 71–72, 74, 88–89, 91–95, 110, 118, 131, 136, 195–197, 200–204, 208–210, 216–224, 236, 245–246, 256–257, 262–263
- Multidimensional quality metrics (MQM)
124–125, 141
- Munday, Jeremy
4, 24, 41, 47, 175, 261–262
- Muñoz, Ricardo
97–99, 101, 103–104, 107–108, 110–112, 114, 200, 228, 243, 249
N
- Non-professional translation
4, 12, 19–20, 22–23, 26–28, 58, 65, 72, 90, 99–100, 108–109, 111–117, 122–124, 126, 129, 131–132, 135–136, 144–145, 148, 154, 161–165, 169–170, 177, 179, 182–183, 185–193, 196, 198–199, 205–212, 220, 222, 224–225, 227–228, 231–232-235, 241–242, 254
- Non-profit organizations (NGO)
13–18, 21–22, 28, 33–36, 52–57, 62, 67, 71, 73, 76–77, 79–80, 83–85, 93–94, 122, 127–128, 132–134, 144–145, 205, 208, 211–212, 217, 219, 224, 236, 240–241, 257
- Nord, Christiane
18, 54, 137, 140, 144, 146–150, 159, 167–168, 183–184, 189, 229–230, 233
O
- O'Brien, Sharon
1, 4–5, 8, 17, 26–27, 33, 36, 38–40, 42, 45, 55, 65, 86–87, 94, 97–98, 106, 113, 117–118, 122–125, 129, 132–133, 139, 158, 163, 195–196, 203, 216–217, 219–223, 236, 245
- O’Hagan, Minako
1, 4–6, 8–9, 19–21, 23–25, 27–29, 31, 35, 42–43, 48–49, 51–52, 54, 59, 61, 78, 91, 112, 145–146, 150, 160, 168, 170, 195, 200–201, 213–214, 225, 227, 229–232, 234, 244
- Olohan, Maeve
1, 27, 56–57, 118, 176, 187, 195, 217, 220–223
- Open source software
66, 70, 83, 216, 235, 252
- Open source software localization
12, 13–14, 19, 28
- Open translation
50, 52, 67–69, 79–80, 132–133, 217
- Orrego-Carmona, David
3–4, 8–9, 22, 30, 35, 81, 134, 136, 181, 185–189, 196–197, 203, 216, 227, 231, 234, 241, 251–252
P
- PACTE
9, 89–91, 109–110, 114–115, 228, 231, 241–254
- PACTE competence model
9, 241, 243–248, 251–254
- Paid crowdsourcing
3, 17, 27–28, 31, 36, 38, 57–60, 67–68, 113, 123, 128–131, 133, 144, 170, 176, 203, 229–230, 257, 260–261
- Participatory cultures
19–20, 37, 45–48, 181
- Pérez-González, Luis
8, 19, 35, 180, 181, 186–188, 190, 193
- Post-anime fansubbing
136, 193
- Post editing
1–3, 5–8, 27–29, 35–36, 45, 50, 59, 62, 66, 68, 73, 77–79, 84–91, 117–118, 121–123, 126–128, 134–136, 140, 145–146, 167–168, 170, 176–177, 206–209, 227–230, 238–239, 240, 252, 259–261
- Monolingual post editing
87–90
- Crowd post editing
27–28, 78, 84–85, 87–90, 117–118, 167–168, 208, 227–228, 252
- Professionalism
6, 109–110, 112, 197, 199, 204, 220, 241–242
- Pym, Anthony
1, 7, 23, 26–27, 93, 107–108, 114, 137–138, 148, 158, 160–166, 196–210, 214, 219–220, 228–230, 239–240, 242–243, 245–246, 251–252, 258–259, 262
R
- Risku, Hanna
18, 26, 97–101, 103–107, 116, 199–200, 217, 229–230, 233, 235–236, 263
- Romhacking
29, 47–48, 213
- Rosetta Foundation
21–23, 31, 36, 65, 67, 73, 76–77, 84, 94, 133, 139, 217, 219–220, 224, 235–236
S
- Scanlations
22, 28–29, 47–50, 213
- Segmentation
63, 66, 77, 135, 140–141, 157, 159–165, 172, 174–175, 193, 252–253
- Shreve, Gregory
9, 19, 97, 106–107, 110–111, 151, 153, 158–159, 161, 174, 231, 235–236, 242–243, 248–249, 252–254
- SMS translation
16, 36, 55–56, 83, 171
- Social networking site
2–3, 23–24, 33–35, 37–38, 44, 46–47, 52–57, 62, 65, 67, 71–76, 83–84, 86, 98, 103–105, 112–113, 128, 134, 140, 142, 147, 153–155, 168, 171, 207–210, 233, 258
- Socio-constructivism
227–235, 238
- Software localization
1, 19, 28, 48–50, 55, 70, 72, 133, 135, 165, 209–210, 216, 252
- Open source software (FLOSS) localization
19, 28, 48–50, 70, 133, 135, 165, 209–210
- Source text
26, 40, 53, 59, 61–63, 67–68, 77, 89–90, 112–113, 119–120, 122, 126–127, 130, 135, 142, 147–150, 153, 157–162, 166–169, 172–173, 180, 182–183, 188–189, 205–206, 239–240, 246–247, 252–253
- Speaklike
33–34, 58, 65, 128–129
- Stepes
5–6, 29, 58–59, 70, 84–86, 133–134, 230, 257
- Subtitling
19, 21–23, 29, 35, 48, 56–57, 65, 79–84, 171, 179–194, 214–217, 225, 240–241, 251–252, 260–261
- Summative evaluation
144, 236
T
- Task-based approaches to education
238–239, 248–249
- TAUS
57, 89–90, 92–93, 123–127, 132–135, 255
- TAUS dynamic quality framework
123–125
- TED Open Translation Initiative
16, 20–21, 30, 35, 70, 73, 79–80, 132, 145, 165, 171, 186, 187, 216, 217, 220–221, 239, 241
- Text types
33–34, 111, 150–153, 225, 243–244, 249–251
- Textual genres
33–36, 150, 152–153, 225, 234, 243–244, 249–253
- Toury, Gideon
103–104, 124, 158, 174, 182–183, 242, 248
- TRANSCOMP
228, 242–248, 253–254
- Transifex
5–6, 22–23, 30–33, 42, 72–73, 83, 134, 234–235
- Translation competence
7–9, 89–90, 99–100, 109–110, 176, 210, 227–228, 230–236, 239–254
- Translation competence models
228, 233–234, 242–248, 251–253
- Translation competence acquisition
8–9, 89–90, 109–110, 176, 231–234, 243, 248–250, 254
- Translation crowdsourcing
2, 8, 11–12, 15–18, 21–25, 29–32, 46–48, 64–73, 84–85, 95, 100–109, 155, 225, 230–231, 251, 255, 257–258
- Micro task crowdsourcing
83, 113–114, 169–170, 175, 207, 259
- Translation management
22–23, 70, 76–77, 86–87, 104, 164–165
- Translation memory (TM)
42, 51–52, 59, 61–63, 65–66, 74, 76–79, 82, 85–87, 101–102, 116–118, 134, 140, 142, 157, 159, 160–165, 169, 172, 176, 212, 230, 245, 251–252
- Translation memory and segmentation
135, 140, 159–164, 172
- Translation networks
31–32, 47–48, 97–98, 101, 103, 105–107, 197, 199–200, 208–209, 222, 234–235, 263
- Translation norms
8, 39, 42–43, 62–63, 69, 107, 112, 121, 123–126, 130–132, 134–136, 146–147, 151, 160, 164, 174, 179–194,198, 206, 209–210, 239, 243, 246–248, 252–253
- Expectancy and professional norms
62–63, 69, 107, 124, 160, 179–190, 193, 209–210
- Translation paradigms
4–5, 7–8, 11–17, 38–39, 42–45, 63–64, 68–70, 84–87, 91, 98–99, 101, 107–108, 113, 121, 124, 127, 130, 135, 141, 148–150, 158, 160–161, 165–166, 168–170, 175, 185, 200–201, 206–208, 213, 216–217, 229–232, 242, 247–248, 261–263
- Translation quality
3, 36, 70, 80, 93, 97, 118, 121–155, 162, 195–196, 202, 217, 237
- Quality tiers
7–8, 126–129, 136–137, 202–203, 258–259
- Quality evaluation
7–8, 74–75, 89, 122–124, 126–127, 129, 131–132, 135–139, 141, 146, 148–151, 154, 158
- Translation technology
4, 7, 22, 42, 50–52, 61–62, 77, 108, 112, 117–118, 125, 159, 199–200, 229–230, 245
- Translation training
1–2, 6, 83, 97, 110–111, 144–145, 158, 218, 227–254
- Translation Turns
5–6, 8, 47, 195–197, 200–204, 261–263
- Activist Turn
47, 203–204
- Economic turn
6, 195, 201–203, 261–263
- Sociological turn
5, 8, 195–197, 200–201, 203, 262
- Technological turn
5, 8, 195, 200–201, 262
- Translation unit
32, 65–67, 71, 114, 172–174, 245
- Cognitive approaches
104, 141–142, 165–166, 172–174, 241, 263
- Comparative linguistic approaches174
- Natural language processing
63, 172
- Translation universals176
- Trommons
22–23, 36, 73, 76–77, 83–84, 134, 139, 234–235, 251–252
U
- Unbabel
6, 29, 58–59, 66, 71, 78, 84–85, 87, 122, 134, 230, 259
- User-generated content
24, 35–36, 44, 87–88, 127–128, 164
- User generated translation
23–24, 28, 54–55, 185
V
- Videogame localization
1, 19, 163–164, 170
- Volunteer motivation
17, 33, 60, 64–65, 72, 74, 92–95, 118, 132–133, 195–196, 202,-204, 216–223, 236
- Volunteer profiles
195–196, 216–217, 223–225
W
- Web localization
1, 4, 33, 103, 148, 154, 158, 164–165, 176, 229–230, 252, 257
- Wikipedia
14–15, 32–34, 44, 52–57, 65, 67–70, 72, 78, 85, 88–89, 134–136, 138–139, 143–144, 167–172, 217, 219–220, 223–224, 236
- Wisdom of the crowd
13–15, 18, 51–52, 57–58, 60, 70, 72–73, 82, 113, 123, 169–170, 238–240
- Workflow approaches
83–85, 140–141
- Crowdsourcing workflows
5, 29–30, 32, 38, 50–53, 55–91, 104–105, 113–114, 116–117, 131–132, 134–136, 140–141, 164–167, 170–172, 174–177, 181, 185–186, 195–196, 202, 206, 257–258