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Journal articles on the topic 'Edit'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 June 2025

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1

Hayes, Tricia, and Tricia Marie Crowley. "EDIT." Journal of Pediatric Nursing 25, no. 2 (2010): e5. http://dx.doi.org/10.1016/j.pedn.2009.12.016.

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2

Hanneder, Jürgen. "To Edit or Not to Edit." École pratique des hautes études. Section des sciences religieuses, no. 124 (September 1, 2017): 51–58. http://dx.doi.org/10.4000/asr.1673.

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3

Cañete Escalona, Felix. "Educación y calidad total, filosofía, principios y herramientas de implementación." Revista Enfoques Educacionales 1, no. 2 (2018): 147. http://dx.doi.org/10.5354/0717-3229.1998.48635.

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Comentario acerca del libro EDUCACIÓN Y CALIDAD TOTAL, FILOSOFÍA, PRINCIPIOS Y HERRAMIENTAS DE IMPLEMENTACIÓN. Autor: José Cruz Ramírez. Edit. Grupo Editorial Iberoamérica, México, 2ª edic. 1997 (558 páginas)
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4

Lang, Annie, Shuhua Zhou, Nancy Schwartz, Paul D. Bolls, and Robert F. Potter. "The Effects of Edits on Arousal, Attention, and Memory for Television Messages: When an Edit Is an Edit Can an Edit Be Too Much?" Journal of Broadcasting & Electronic Media 44, no. 1 (2000): 94–109. http://dx.doi.org/10.1207/s15506878jobem4401_7.

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5

Gershon, Ilana. "Editors Edit." Anthropology News 59, no. 3 (2018): e241-e244. http://dx.doi.org/10.1111/an.850.

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6

Brink, Dean A. "Film Edit." New Writing 12, no. 3 (2015): 293. http://dx.doi.org/10.1080/14790726.2015.1051996.

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7

Lyu, Jun. "Edit compactness." Nature Plants 6, no. 3 (2020): 180. http://dx.doi.org/10.1038/s41477-020-0623-5.

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8

Rahman, Haseeb, Aish Sinha, Ozan M. Demir, and Divaka Perera. "EDIT-CMD." JACC: Cardiovascular Imaging 15, no. 9 (2022): 1674–76. http://dx.doi.org/10.1016/j.jcmg.2022.04.027.

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9

Hofmann, Martin, Benjamin Pierce, and Daniel Wagner. "Edit lenses." ACM SIGPLAN Notices 47, no. 1 (2012): 495–508. http://dx.doi.org/10.1145/2103621.2103715.

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10

Gooding, Mark. "Help Edit." Electronics Education 1998, no. 3 (1998): 38. http://dx.doi.org/10.1049/ee.1998.0072.

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11

Martin, Geoffrey A., Selina Tsim, Andrew C. Kidd, et al. "Pre-EDIT." Chest 156, no. 6 (2019): 1204–13. http://dx.doi.org/10.1016/j.chest.2019.07.010.

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12

B, Saugat. "Inside Edit." Scientific American 326, no. 5 (2022): 16. http://dx.doi.org/10.1038/scientificamerican0522-16b.

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13

Akutsu, Tatsuya, Daiji Fukagawa, and Atsuhiro Takasu. "Approximating Tree Edit Distance through String Edit Distance." Algorithmica 57, no. 2 (2008): 325–48. http://dx.doi.org/10.1007/s00453-008-9213-z.

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14

Cortés, Xavier, Donatello Conte, and Hubert Cardot. "Learning edit cost estimation models for graph edit distance." Pattern Recognition Letters 125 (July 2019): 256–63. http://dx.doi.org/10.1016/j.patrec.2019.05.001.

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15

Whalen, E. "Why we edit." American Journal of Roentgenology 152, no. 3 (1989): 647–49. http://dx.doi.org/10.2214/ajr.152.3.647.

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16

Jie Wei. "Markov edit distance." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 3 (2004): 311–21. http://dx.doi.org/10.1109/tpami.2004.1262315.

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17

Yates, Steven. "Edit Film Culture!" Afterimage 45, no. 5 (2018): 4–5. http://dx.doi.org/10.1525/aft.2018.45.5.4.

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18

Wark, McKenzie. "The Insolent Edit." Grey Room 52 (July 2013): 83–93. http://dx.doi.org/10.1162/grey_a_00117.

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19

Mahrer, Kenneth D. "Edit! Don't fiddle." Leading Edge 23, no. 9 (2004): 852–53. http://dx.doi.org/10.1190/1.1803495.

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20

Leslie, Mitch. "Edit the messenger." Science 386, no. 6720 (2024): 368–71. http://dx.doi.org/10.1126/science.adu0428.

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21

Livio, Oren, and Shani Cohen-Yechezkely. "Copy, Edit, Paste." Journalism Studies 20, no. 5 (2018): 696–713. http://dx.doi.org/10.1080/1461670x.2017.1417054.

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22

Lucas, S. B. "Leprosy (2nd edit.)." Transactions of the Royal Society of Tropical Medicine and Hygiene 81, no. 3 (1987): 430. http://dx.doi.org/10.1016/0035-9203(87)90159-3.

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23

Leyshon, Andrew. "Why edit handbooks?" Regional Studies 47, no. 9 (2013): 1613–14. http://dx.doi.org/10.1080/00343404.2013.849366.

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24

Halling, Dan C. "Cool Edit 2000." Ear and Hearing 21, no. 5 (2000): 537–38. http://dx.doi.org/10.1097/00003446-200010000-00017.

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25

Atlas, Marshall. "The user edit." ACM SIGDOC Asterisk Journal of Computer Documentation 22, no. 3 (1998): 5–6. http://dx.doi.org/10.1145/381808.381811.

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26

Martínez, Emma. "To edit or not to edit. That is the question." Ciències: revista del professorat de ciències de Primària i Secundària, no. 37 (May 31, 2019): 30. http://dx.doi.org/10.5565/rev/ciencies.407.

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27

De Almeida Costa, Felipe, Adriana S Pagano, Thiago Castro Ferreira, and Wagner Meira, Jr. "A Human-machine Cooperation Protocol for Machine Translation Output Edit Annotation." Tradumàtica: tecnologies de la traducció, no. 19 (December 31, 2021): 148–70. http://dx.doi.org/10.5565/rev/tradumatica.292.

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We report on a study exploring automatic edit annotation in a post-editing corpus with a new method for computing edit types. We examine edit type association with quality scores assigned to the machine translation output and the post-edited texts. Finally, we account for shortcomings in our method and point out edit types worth leveraging.
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28

Garcia-Hernandez, Carlos, Alberto Fernández, and Francesc Serratosa. "Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening." Current Topics in Medicinal Chemistry 20, no. 18 (2020): 1582–92. http://dx.doi.org/10.2174/1568026620666200603122000.

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Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. Methods: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.
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29

Hyyrö, Heikki, and Shunsuke Inenaga. "Dynamic RLE-Compressed Edit Distance Tables Under General Weighted Cost Functions." International Journal of Foundations of Computer Science 29, no. 04 (2018): 623–45. http://dx.doi.org/10.1142/s0129054118410083.

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Kim and Park [A dynamic edit distance table, J. Disc. Algo., 2:302–312, 2004] proposed a method (KP) based on a “dynamic edit distance table” that allows one to efficiently maintain unit cost edit distance information between two strings [Formula: see text] of length [Formula: see text] and [Formula: see text] of length [Formula: see text] when the strings can be modified by single-character edits to their left or right ends. This type of computation is useful e.g. in cyclic string comparison. KP uses linear time, [Formula: see text], to update the distance representation after each single edit. Recently Hyyrö et al. [Incremental string comparison, J. Disc. Algo., 34:2-17, 2015] presented an efficient method for maintaining the dynamic edit distance table under general weighted edit distance, running in [Formula: see text] time per single edit, where [Formula: see text] is the maximum weight of the cost function. The work noted that the [Formula: see text] space requirement, and not the running time, may be the main bottleneck in using the dynamic edit distance table. In this paper we take the first steps towards reducing the space usage of the dynamic edit distance table by RLE compressing [Formula: see text] and [Formula: see text]. Let [Formula: see text] and [Formula: see text] be the lengths of RLE compressed versions of [Formula: see text] and [Formula: see text], respectively. We propose how to store the dynamic edit distance table using [Formula: see text] space while maintaining the same time complexity as the previous methods for uncompressed strings.
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30

SOLÉ-RIBALTA, ALBERT, FRANCESC SERRATOSA, and ALBERTO SANFELIU. "ON THE GRAPH EDIT DISTANCE COST: PROPERTIES AND APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 05 (2012): 1260004. http://dx.doi.org/10.1142/s021800141260004x.

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We model the edit distance as a function in a labeling space. A labeling space is an Euclidean space where coordinates are the edit costs. Through this model, we define a class of cost. A class of cost is a region in the labeling space that all the edit costs have the same optimal labeling. Moreover, we characterize the distance value through the labeling space. This new point of view of the edit distance gives us the opportunity of defining some interesting properties that are useful for a better understanding of the edit distance. Finally, we show the usefulness of these properties through some applications.
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31

HAN, YO-SUB, SANG-KI KO, and KAI SALOMAA. "THE EDIT-DISTANCE BETWEEN A REGULAR LANGUAGE AND A CONTEXT-FREE LANGUAGE." International Journal of Foundations of Computer Science 24, no. 07 (2013): 1067–82. http://dx.doi.org/10.1142/s0129054113400315.

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The edit-distance between two strings is the smallest number of operations required to transform one string into the other. The distance between languages L1and L2is the smallest edit-distance between string wi∈ Li, i = 1, 2. We consider the problem of computing the edit-distance of a given regular language and a given context-free language. First, we present an algorithm that finds for the languages an optimal alignment, that is, a sequence of edit operations that transforms a string in one language to a string in the other. The length of the optimal alignment, in the worst case, is exponential in the size of the given grammar and finite automaton. Then, we investigate the problem of computing only the edit-distance of the languages without explicitly producing an optimal alignment. We design a polynomial time algorithm that calculates the edit-distance based on unary homomorphisms.
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32

Lieberman, Michael, and Jimmy Lin. "You Are Where You Edit: Locating Wikipedia Contributors through Edit Histories." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (2009): 106–13. http://dx.doi.org/10.1609/icwsm.v3i1.13952.

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Whether knowingly or otherwise, Wikipedia contributors reveal their interests and expertise through their contribution patterns. An analysis of Wikipedia edit histories shows that it is often possible to associate contributors with relatively small geographic regions, usually corresponding to where they were born or where they presently live. For many contributors, the geographic coordinates of pages they have edited are tightly clustered. Results suggest that a wealth of information about contributors can be gleaned from edit histories. This illustrates the efficacy of data mining on large, publicly-available datasets and raises potential privacy concerns.
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33

Duehr, K., J. Grimminger, S. Rapp, A. Albers, and N. Bursac. "Enabling Distributed Teams – A Process Model for Early and Continuous Method Validation." Proceedings of the Design Society 2 (May 2022): 161–70. http://dx.doi.org/10.1017/pds.2022.17.

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AbstractNeglecting challenges of distributed collaboration can lead to significant efficiency and effectiveness losses in agile, distributed development teams. The EDiT method provides support for improving distributed collaboration of development teams. To ensure acceptance, applicability, and contribution to success in industrial development practice, it is necessary to validate the EDiT method. The goal of this contribution is the development of a process model for early and incremental validation of the EDiT method in the field finally leading to a validation of the EDiT method itself.
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34

Khalid, Madiha, Muhammad Murtaza Yousaf, and Muhammad Umair Sadiq. "Toward Efficient Similarity Search under Edit Distance on Hybrid Architectures." Information 13, no. 10 (2022): 452. http://dx.doi.org/10.3390/info13100452.

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Edit distance is the most widely used method to quantify similarity between two strings. We investigate the problem of similarity search under edit distance. Given a collection of sequences, the goal of similarity search under edit distance is to find sequences in the collection that are similar to a given query sequence where the similarity score is computed using edit distance. The canonical method of computing edit distance between two strings uses a dynamic programming-based approach that runs in quadratic time and space, which may not provide results in a reasonable amount of time for large sequences. It advocates for parallel algorithms to reduce the time taken by edit distance computation. To this end, we present scalable parallel algorithms to support efficient similarity search under edit distance. The efficiency and scalability of the proposed algorithms is demonstrated through an extensive set of experiments on real datasets. Moreover, to address the problem of uneven workload across different processing units, which is mainly caused due to the significant variance in the size of the sequences, different data distribution schemes are discussed and empirically analyzed. Experimental results have shown that the speedup achieved by the hybrid approach over inter-task and intra-task parallelism is 18 and 13, respectively.
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35

Winter, Felix, Nysret Musliu, and Peter Stuckey. "Explaining Propagators for String Edit Distance Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1676–83. http://dx.doi.org/10.1609/aaai.v34i02.5530.

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The computation of string similarity measures has been thoroughly studied in the scientific literature and has applications in a wide variety of different areas. One of the most widely used measures is the so called string edit distance which captures the number of required edit operations to transform a string into another given string. Although polynomial time algorithms are known for calculating the edit distance between two strings, there also exist NP-hard problems from practical applications like scheduling or computational biology that constrain the minimum edit distance between arrays of decision variables. In this work, we propose a novel global constraint to formulate restrictions on the minimum edit distance for such problems. Furthermore, we describe a propagation algorithm and investigate an explanation strategy for an edit distance constraint propagator that can be incorporated into state of the art lazy clause generation solvers. Experimental results show that the proposed propagator is able to significantly improve the performance of existing exact methods regarding solution quality and computation speed for benchmark problems from the literature.
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Chuzaimah Zulkifli, Umi. "Pengembangan Modul PreprocessingTeks untuk Kasus Formalisasi dan Pengecekan Ejaan Bahasa Indonesia pada Aplikasi Web Mining Simple Solution (WMSS)." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (2018): 95. http://dx.doi.org/10.20956/jmsk.v15i2.5718.

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Abstract Data of social media currently has been much used to analyze both sentiment analysis and another analysis. In fact, data that is obtained from the social media in generally has some mistakes which can influence the spelling in writing of words. The solution offered is word formalization and spelling check. Based on the problem, it will be built a preprocessing model to overcome two the mistakes. The method that will be used in formalization is to change the words to be formal form based on KBBI, while the method used for spelling check is spelling correction. Spelling correction method consists of distance edit, bigram and distance edit rule. In this study, in addition the application of both methods, also it will be analyzed comparing the result of spelling correction. From the result of analysis shows that distance edit rule has higher accuracy, namely 83.39% than using both edit distance and bigram method. In addition, edit distance rule method also has faster performance than another both methods. Overall, method to change word to formal word were based on KBBI and spelling correction has been able to overcome the problem of two cases, such that it can increase accuracy of the result of the analysis. Keywords: preprocessing, spelling correction, edit distance, bigram AbstrakData media sosial saat ini telah banyak digunakan untuk melakukan analisis baik analisis sentimen maupun analisis terkait lainnya. Nyatanya, data yang diperoleh dari media sosial tersebut pada umumnya memiliki kesalahan yang akan mempengaruhi hasil analisis. Kesalahan tersebut berupa penggunaan kata yang tidak baku dan adanya kesalahan ejaan dalam penulisan kata. Solusi yang ditawarkan berupa formalisasi kata dan pengecekan ejaan. Berdasarkan masalah tersebut, akan dibangun modul preprocessing untuk mengatasi dua kesalahan di atas. Metode yang digunakan pada formalisasi adalah mengubah kata ke bentuk formal berdasarkan KBBI sedangkan metode yang digunakan pada pengecekan ejaan adalah spelling correction. Metode spelling correction tersebut terdiri dari tiga yaitu edit distance, bigram dan edit distance + rule. Pada penelitian ini, selain penerapan kedua metode juga akan dilakukan analisis untuk melihat perbandingan hasil pada metode spelling correction. Dari hasil analisis tersebut, diketahui bahwa metode edit distance + rule memiliki akurasi yang lebih tinggi yaitu sebesar 83,39% dibandingkan dengan kedua metode lainnya yaitu edit distance dan bigram. Selain itu, metode edit distance + rule juga memiliki performa tercepat dibandingkan kedua metode lainnya. Secara keseluruhan, metode mengubah kata ke bentuk formal berdasarkan KBBI dan spelling correction telah mampu mengatasi masalah pada dua kasus di atas sehingga dapat meningkatkan akurasi hasil analisis. Kata Kunci:preprocessing, spelling correction, edit distance, bigram
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37

Hoover, Brett A., and Paul B. Connor. "Analyzing edit queries to identify problems in data collection, edits, and management." Controlled Clinical Trials 12, no. 5 (1991): 649. http://dx.doi.org/10.1016/0197-2456(91)90152-c.

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38

Obregón-Sierra, Ángel, and Natalia González-Fernández. "¿Por qué no editan en la Wikipedia los alumnos universitarios?" Tripodos, no. 46 (February 5, 2021): 13–28. http://dx.doi.org/10.51698/tripodos.2020.46p13-28.

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Los alumnos universitarios admiten que una de las herramientas que más utilizan en su vida diaria es la Wikipedia, una enciclopedia en línea que da libertad para que cualquier persona pueda editar su contenido. Consideran que esta es fiable y útil para sus consultas, pero sobre todo destacan que contiene datos actuales. A pesar de estos aspectos positivos, se considera que menos del 1% de los alumnos edita frecuentemente en ella para mejorar sus contenidos. Para conocer los motivos por los cuales no editan en ella, en esta investigación hemos implementado el método mixto secuencial, para obtener información de tres herramientas de obtención de datos como son los cuestionarios, los grupos de discusión y las entrevistas. Una vez analizadas las 1173 respuestas de los cuestionarios, realizamos un grupo de discusión con los alumnos de la Universidad de Barcelona y entrevistas a expertos editores en la Wikipedia, y se constató que solo el 0,38% edita frecuentemente o muy a menudo. Se expusieron varios motivos para no editar en ella, pero principalmente los alumnos indicaron que no se sentían capaces de aportar ningún tipo de información. Why Don’t University Students Edit on Wikipedia? College students admit that one of the tools they use the most in their daily lives is Wikipedia, an online encyclopedia that gives anyone the freedom to edit its content. They consider that it is reliable and useful for their queries, but above all they believe that it contains current data. In spite of these positive aspects, it is considered that less than 1% of the students frequently edit it to improve its contents. In order to determine the reasons why they do not edit it, in this research we have implemented the sequential mixed method to obtain information from three data collection tools such as questionnaires, discussion groups and interviews. Once the 1,173 responses to the questionnaires had been analyzed, we carried out a discussion group with students from the University of Barcelona and interviews with expert editors on Wikipedia, and it was found that only 0.38% edited frequently or very often. Several reasons were given for not editing it, but mainly the students indicated that they did not feel capable of providing any type of information. Key words: Wikipedia, Wikis, Web 2.0, university students, collaborative work. Paraules clau: Wikipedia, Wikis, Web 2.0, estudiantes universitarios, trabajo colaborativo
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39

Raschka, Achim, and Dirk Franke. "Edit-Wars in Wikipedia." Zeitschrift für Ideengeschichte 10, no. 2 (2016): 17–24. http://dx.doi.org/10.17104/1863-8937-2016-2-17.

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Die Wikipedia ist im Jahr 2002 angetreten, um den bereits etablierten Internet-Enzyklopädien etwas völlig Neues entgegenzusetzen, ja die bislang geübte Praxis der Enzyklopädistik zu revolutionieren. Vor allem in zwei wesentlichen Punkten weicht die Wikipedia von konventionellen Lexika ab: Zum einen stehen ihre Inhalte vollständig unter freier Lizenz, was bedeutet, dass sie auch wörtlich weiterverwendet und weiterverarbeitet werden dürfen. Zum anderen ist der Autorenkreis nicht auf einige wenige Experten beschränkt. Ziel der Wikipedia ist es, dass sich jeder an der Erstellung und Aktualisierung von Artikeln beteiligen kann. Jeder soll unmittelbar und ohne Vorkontrolle zum Gesamtbestand des Wissens beitragen können.
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40

McGrane, Martin, and Michael A. Charleston. "Biological Network Edit Distance." Journal of Computational Biology 23, no. 9 (2016): 776–88. http://dx.doi.org/10.1089/cmb.2016.0062.

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41

Rappaport Hovav, Malka. "In memoriam—Edit Doron." Brill’s Journal of Afroasiatic Languages and Linguistics 11, no. 2 (2019): 243–47. http://dx.doi.org/10.1163/18776930-01102001.

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42

Kintisch, E. "Paulson: Edit Out Credit." Science 317, no. 5840 (2007): 883c. http://dx.doi.org/10.1126/science.317.5840.883c.

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43

Ristad, E. S., and P. N. Yianilos. "Learning string-edit distance." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 5 (1998): 522–32. http://dx.doi.org/10.1109/34.682181.

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Myers, R., R. C. Wison, and E. R. Hancock. "Bayesian graph edit distance." IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 6 (2000): 628–35. http://dx.doi.org/10.1109/34.862201.

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45

Schwarz, Diemo, and Etienne Brunet. "theconcatenator: Placard XP edit." Leonardo Music Journal 18 (December 2008): 89–90. http://dx.doi.org/10.1162/lmj.2008.18.89b.

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46

Chen, Xiaowu, Dongqing Zou, Qinping Zhao, and Ping Tan. "Manifold preserving edit propagation." ACM Transactions on Graphics 31, no. 6 (2012): 1–7. http://dx.doi.org/10.1145/2366145.2366151.

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47

Hayes, John R. "Atlas's “the user edit”." ACM SIGDOC Asterisk Journal of Computer Documentation 22, no. 3 (1998): 7–9. http://dx.doi.org/10.1145/381808.381812.

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48

Winter, R. "Edit a staff round." BMJ 303, no. 6812 (1991): 1258–59. http://dx.doi.org/10.1136/bmj.303.6812.1258.

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49

Akerson, David. "Fundamentals of Circuit Edit." EDFA Technical Articles 25, no. 2 (2023): 9–13. http://dx.doi.org/10.31399/asm.edfa.2023-2.p009.

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50

Vid, Stropnik, and Oražem Maruša. "[Re] Graph Edit Networks." ReScience C 8, no. 2 (2022): #38. https://doi.org/10.5281/zenodo.6574701.

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