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Journal articles on the topic 'Models evaluation'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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1

Stufflebeam, Daniel. "Evaluation Models." New Directions for Evaluation 2001, no. 89 (2001): 7. http://dx.doi.org/10.1002/ev.3.

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2

Contandriopoulos, Damien, and Astrid Brousselle. "Evaluation models and evaluation use." Evaluation 18, no. 1 (January 2012): 61–77. http://dx.doi.org/10.1177/1356389011430371.

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3

Albers, J., S. Hassfeld, and C. F. Vahl. "EVALUATION OF MODELS." Biomedizinische Technik/Biomedical Engineering 47, s1b (2002): 919–22. http://dx.doi.org/10.1515/bmte.2002.47.s1b.919.

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4

Hansen, Hanne Foss. "Choosing Evaluation Models." Evaluation 11, no. 4 (October 2005): 447–62. http://dx.doi.org/10.1177/1356389005060265.

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5

Gandomkar, Roghayeh, Mohammad Jalili, and Azim Mirzazadeh. "Evaluating assessment programmes using programme evaluation models." Medical Teacher 37, no. 8 (May 29, 2015): 792–93. http://dx.doi.org/10.3109/0142159x.2015.1042436.

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6

Gallegos, Arnold. "Meta-evaluation of school evaluation models." Studies in Educational Evaluation 20, no. 1 (January 1994): 41–54. http://dx.doi.org/10.1016/s0191-491x(00)80004-8.

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7

Ricolfe-Viala, Carlos, and Antonio-Jose Sanchez-Salmeron. "Lens distortion models evaluation." Applied Optics 49, no. 30 (October 19, 2010): 5914. http://dx.doi.org/10.1364/ao.49.005914.

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8

Wagener, Thorsten. "Evaluation of catchment models." Hydrological Processes 17, no. 16 (2003): 3375–78. http://dx.doi.org/10.1002/hyp.5158.

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9

House, E. R. "Economic models of evaluation." Scandinavian Journal of Social Welfare 7, no. 2 (April 1998): 110–13. http://dx.doi.org/10.1111/j.1468-2397.1998.tb00210.x.

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10

Granger, Clive W. J., and Yongil Jeon. "Evaluation of global models." Economic Modelling 24, no. 6 (November 2007): 980–89. http://dx.doi.org/10.1016/j.econmod.2007.03.008.

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11

Pesaran, M. H., and R. P. Smith. "Evaluation of macroeconometric models." Economic Modelling 2, no. 2 (April 1985): 125–34. http://dx.doi.org/10.1016/0264-9993(85)90018-5.

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12

Labys, Walter C. "1.3. Evaluation of models." Energy 15, no. 3-4 (March 1990): 155–62. http://dx.doi.org/10.1016/0360-5442(90)90079-h.

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13

Nie, Yixin, Yicheng Wang, and Mohit Bansal. "Analyzing Compositionality-Sensitivity of NLI Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6867–74. http://dx.doi.org/10.1609/aaai.v33i01.33016867.

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Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.
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14

Sepúlveda, Carlos, Oscar Montiel, José M. Cornejo Bravo, and Roberto Sepúlveda. "Fuzzy Evaluation of Pharmacokinetic Models." Computational Intelligence and Neuroscience 2018 (November 1, 2018): 1–10. http://dx.doi.org/10.1155/2018/1983897.

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Population pharmacokinetic (PopPK) models allow researchers to predict and analyze drug behavior in a population of individuals and to quantify the different sources of variability among these individuals. In the development of PopPK models, the most frequently used method is the nonlinear mixed effect model (NLME). However, once the PopPK model has been developed, it is necessary to determine if the selected model is the best one of the developed models during the population pharmacokinetic study, and this sometimes becomes a multiple criteria decision making (MCDM) problem, and frequently, researchers use statistical evaluation criteria to choose the final PopPK model. The used evaluation criteria mentioned above entail big problems since the selection of the best model becomes susceptible to the human error mainly by misinterpretation of the results. To solve the previous problems, we introduce the development of a software robot that can automate the task of selecting the best PopPK model considering the knowledge of human expertise. The software robot is a fuzzy expert system that provides a method to systematically perform evaluations on a set of candidate PopPK models of commonly used statistical criteria. The presented results strengthen our hypothesis that the software robot can be successfully used to evaluate PopPK models ensuring the selection of the best PopPK model.
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15

Renger, Ralph, Shandiin Wood, Simon Williamson, and Stefanie Krapp. "Systemic Evaluation, Impact Evaluation and Logic Models." Evaluation Journal of Australasia 11, no. 2 (September 2011): 24–30. http://dx.doi.org/10.1177/1035719x1101100204.

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16

Přibyl, J., H. Krejčová, J. Přibylova, I. Misztal, S. Tsuruta, and N. Mielenz. "Models for evaluation of growth of performance tested bulls." Czech Journal of Animal Science 53, No. 2 (February 7, 2008): 45–54. http://dx.doi.org/10.17221/331-cjas.

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Before being used for insemination, young bulls of Czech Fleckvieh (CF) are tested for growth at performance-test stations. While at stations, the bulls are weighed monthly. Evaluation included 7 448 bulls with 82 676 records of weight measured from 6 to 520 days of life. In the station-year-period (HYS), which can be prolonged up to 3 months, different groups were tested according to the beginning of growth curve and according to test-days of weighing. Weight analyses were used to handle heterogeneous variability based on age. Legendre Polynomials (LP) with 5 parameters described the average growth curve for HYS classes. Deviations from average curves were decomposed into genetic (G), animal’s permanent environment (PE) and residual (RES) components. Functions of (G) and (PE) were tested using LP random regression (RR) methodology with 5 or 3 parameters and Linear Spline (SP) function with 5 knots. Variance increases with the age of the animals. From 100 to 400 days, heritability was nearly the same with a mild depression in the middle of the period. The average was <I>h</I><sup>2</sup> = 0.31 and ended with <I>h</I><sup>2</sup> = 0.36. Results were similar for variance components, heritability, genetic, environmental and phenotype correlations from different models with different LP and SP functions. Higher RES variability occurred only for LP with 3 parameters. For traits like live weight, the RR should have at least 3 parameters and SP function should be used.
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17

Bojcetic, Nenad, Filip Valjak, Dragan Zezelj, and Tomislav Martinec. "Automatized Evaluation of Students’ CAD Models." Education Sciences 11, no. 4 (March 24, 2021): 145. http://dx.doi.org/10.3390/educsci11040145.

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The article describes an attempt to address the automatized evaluation of student three-dimensional (3D) computer-aided design (CAD) models. The driving idea was conceptualized under the restraints of the COVID pandemic, driven by the problem of evaluating a large number of student 3D CAD models. The described computer solution can be implemented using any CAD computer application that supports customization. Test cases showed that the proposed solution was valid and could be used to evaluate many students’ 3D CAD models. The computer solution can also be used to help students to better understand how to create a 3D CAD model, thereby complying with the requirements of particular teachers.
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18

Lin, Qi, and Zhi Li. "Credibility Evaluation of Simulation Models." Advanced Materials Research 765-767 (September 2013): 713–16. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.713.

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This paper discusses an evaluation methodology that can be used to assess the credibility of simulation models. The goal of verification and validation of simulation models will be described, together with a description of evaluation methodology and evaluation procedures. The credibility of simulation models can be evaluated using the method proposed here, which will be much meaningful to the simulation systems development. Taking a space Tether-net simulation system as an instance, the credibility evaluation result is provided.
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19

Sweeting, Trevor. "Statistical Models for Nondestructive Evaluation." International Statistical Review / Revue Internationale de Statistique 63, no. 2 (August 1995): 199. http://dx.doi.org/10.2307/1403614.

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20

Vanni, Tazio, Jonathan Karnon, Jason Madan, Richard G. White, W. John Edmunds, Anna M. Foss, and Rosa Legood. "Calibrating Models in Economic Evaluation." PharmacoEconomics 29, no. 1 (January 2011): 35–49. http://dx.doi.org/10.2165/11584600-000000000-00000.

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21

Karnon, Jonathan, and Tazio Vanni. "Calibrating Models in Economic Evaluation." PharmacoEconomics 29, no. 1 (January 2011): 51–62. http://dx.doi.org/10.2165/11584610-000000000-00000.

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22

Jayeola, Dare. "Evaluation of Asset Allocation Models." NIPES Journal of Science and Technology Research 2, no. 3 (August 31, 2020): 328. http://dx.doi.org/10.37933/nipes/2.3.2020.31.

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23

TANAKA, Hirayatu. "“Kansei” Evaluation and Pychological Models." Japanese journal of ergonomics 30, Supplement (1994): 36–37. http://dx.doi.org/10.5100/jje.30.supplement_36.

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24

Balendra, R., and M. A. Hijazi. "Evaluation of injection upsetting models." International Journal of Machine Tools and Manufacture 29, no. 3 (January 1989): 345–60. http://dx.doi.org/10.1016/0890-6955(89)90005-9.

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25

Zwick, Rami. "The evaluation of verbal models." International Journal of Man-Machine Studies 29, no. 2 (August 1988): 149–57. http://dx.doi.org/10.1016/s0020-7373(88)80043-9.

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26

Harris, David Money, Nancy Wei, Zunyan Wang, Austin Fikes, and Avi Thaker. "Evaluation of predictive technology models." Microelectronics Journal 80 (October 2018): 7–17. http://dx.doi.org/10.1016/j.mejo.2018.06.002.

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27

Ten Eyck, Lynn, and Martin Perez. """"Objective"" Evaluation of Macromolecular Models"." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C346. http://dx.doi.org/10.1107/s2053273314096533.

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"Evaluation of the accuracy and quality of molecular models from X-ray crystallography is a continuing problem. The refinement process as currently practiced is inherently biased through use of the model to provide phase information for the refinement process. To investigate the extent of model bias we are developing model-free criteria for detailed analysis of crystal structures. We present preliminary findings from comparing a set of high resolution, well-refined structures from the Joint Center for Structural Genomics repository to maps based solely on phases from selenomethionine anomalous scattering. Removal of atoms with little or no support in the ""objective"" map reliably improves or does not degrade the free R-factor. The selenomethionine map, or any other map produced without the assumption of a molecular model, can be used to identify features supported by diffraction data and those which come from the application of structural constraints."
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28

DUYM, STEFAAN, RANDY STIENS, and KOENRAAD REYBROUCK. "Evaluation of Shock Absorber Models." Vehicle System Dynamics 27, no. 2 (February 1997): 109–27. http://dx.doi.org/10.1080/00423119708969325.

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29

BANKER, RAJIV D. "Selection of efficiency evaluation models." Contemporary Accounting Research 9, no. 1 (September 1992): 343–55. http://dx.doi.org/10.1111/j.1911-3846.1992.tb00885.x.

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30

Boyle, Kerrie Eileen, and Thomas B. Starr. "Survival Models for Fertility Evaluation." Journal of the American Statistical Association 80, no. 392 (December 1985): 823–27. http://dx.doi.org/10.1080/01621459.1985.10478189.

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31

Gu, Hong-Qiu, and Chelsea Liu. "Clinical prediction models: evaluation matters." Annals of Translational Medicine 8, no. 4 (February 2020): 72. http://dx.doi.org/10.21037/atm.2019.11.143.

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32

Tian, J. "Quality-evaluation models and measurements." IEEE Software 21, no. 3 (May 2004): 84–91. http://dx.doi.org/10.1109/ms.2004.1293078.

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33

Wallis, Anne Baber. "Evaluation Theory, Models, and Applications." JAMA 299, no. 22 (June 11, 2008): 2692. http://dx.doi.org/10.1001/jama.299.22.2692.

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34

Swift, D. C., and S. J. White. "An Evaluation of Detonation Models." Le Journal de Physique IV 05, no. C4 (May 1995): C4–37—C4–47. http://dx.doi.org/10.1051/jp4:1995403.

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35

Hockey, L. "Nursing Models: Analysis and Evaluation." Journal of Medical Ethics 12, no. 1 (March 1, 1986): 51. http://dx.doi.org/10.1136/jme.12.1.51-a.

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36

Kusiak, A., and A. Zakarian. "Reliability evaluation of process models." IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A 19, no. 2 (June 1996): 268–75. http://dx.doi.org/10.1109/95.506113.

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37

Candoli, Carl, Karen Cullen, and Daniel L. Stueflebeam. "Analysis of alternative evaluation models." Journal of Personnel Evaluation in Education 9, no. 4 (December 1995): 369–81. http://dx.doi.org/10.1007/bf00973022.

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38

Eloubaidy, A. F., S. M. Hussain, and M. T. Al-Taie. "Field evaluation of desalinization models." Agricultural Water Management 24, no. 1 (September 1993): 1–13. http://dx.doi.org/10.1016/0378-3774(93)90057-h.

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39

Vand, Alireza Sepah, Parveen Sihag, Balraj Singh, and Mehran Zand. "Comparative Evaluation of Infiltration Models." KSCE Journal of Civil Engineering 22, no. 10 (July 7, 2018): 4173–84. http://dx.doi.org/10.1007/s12205-018-1347-1.

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40

Fantozzi, R., and S. Brunelleschi. "Cellular models for drug evaluation." Pharmacological Research 22 (September 1990): 190. http://dx.doi.org/10.1016/1043-6618(90)90226-4.

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41

Raj, Phani K., and John A. Morris. "Computerized spill hazard evaluation models." Journal of Hazardous Materials 25, no. 1-2 (January 1990): 77–92. http://dx.doi.org/10.1016/0304-3894(90)85071-a.

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42

DeSilets, Lynore D. "Another Look at Evaluation Models." Journal of Continuing Education in Nursing 41, no. 1 (January 1, 2010): 12–13. http://dx.doi.org/10.3928/00220124-20091222-02.

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43

Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — training and evaluation of hidden Markov models." Nature Methods 17, no. 2 (February 2020): 121–22. http://dx.doi.org/10.1038/s41592-019-0702-6.

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44

Linzalone, Roberto, and Giovanni Schiuma. "A review of program and project evaluation models." Measuring Business Excellence 19, no. 3 (August 17, 2015): 90–99. http://dx.doi.org/10.1108/mbe-04-2015-0024.

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Purpose – This paper aims to review Program and Project evaluation Models. The assessment of the Evaluation Model (metaevaluation) is a critical step in Evaluation, as it is at the basis of a successful Program/Project evaluation. A wide and effective review of EMs is a basic, as well as fundamental, support in meta-evaluation that affects positively the overall evaluation efficacy and efficiency. Despite a large number of reviews of EMs and a numerous population of EMs, developed in heterogeneous projects and programs settings, the literature lacks comprehensive collections and reviews of EMs that this paper addresses to provide a basis for the assessment of EMs. Design/methodology/approach – Through a systematic literature review carried out via the Internet, and querying search engines, several models addressing program or project evaluation have been identified and analyzed. Following a process of normalization of the results gathered, they have been analyzed and compared according to key descriptive issues. They have been, at the end, summarized and rationalized in a comprehensive frame. Findings – In recent years, evaluation studies have focused on the explanation of the mechanisms that underlie the transformation of projects’ and programs’ outputs into socio-economic effects, arguing that making them explicit allows to understand why a project or program is successful, as well as evaluating its extent. To assess and explain program’s and project’s effects, a basic, although fundamental, role in evaluation is played by the EM. A wide and heterogeneous set of 57 EMs has been identified, defined and framed in typologies, according to a systematic review research. Originality/value – The approach to the review of EMs and the definition of a boundary of interest for management and economic researchers and practitioners represent an original issue of this paper.
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45

VIRPIOJA, SAMI, MARI-SANNA PAUKKERI, ABHISHEK TRIPATHI, TIINA LINDH-KNUUTILA, and KRISTA LAGUS. "Evaluating vector space models with canonical correlation analysis." Natural Language Engineering 18, no. 3 (September 20, 2011): 399–436. http://dx.doi.org/10.1017/s1351324911000271.

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AbstractVector space models are used in language processing applications for calculating semantic similarities of words or documents. The vector spaces are generated with feature extraction methods for text data. However, evaluation of the feature extraction methods may be difficult. Indirect evaluation in an application is often time-consuming and the results may not generalize to other applications, whereas direct evaluations that measure the amount of captured semantic information usually require human evaluators or annotated data sets. We propose a novel direct evaluation method based on canonical correlation analysis (CCA), the classical method for finding linear relationship between two data sets. In our setting, the two sets are parallel text documents in two languages. A good feature extraction method should provide representations that reflect the semantic content of the documents. Assuming that the underlying semantic content is independent of the language, we can study feature extraction methods that capture the content best by measuring dependence between the representations of a document and its translation. In the case of CCA, the applied measure of dependence is correlation. The evaluation method is based on unsupervised learning, it is language- and domain-independent, and it does not require additional resources besides a parallel corpus. In this paper, we demonstrate the evaluation method on a sentence-aligned parallel corpus. The method is validated by showing that the obtained results with bag-of-words representations are intuitive and agree well with the previous findings. Moreover, we examine the performance of the proposed evaluation method with indirect evaluation methods in simple sentence matching tasks, and a quantitative manual evaluation of word translations. The results of the proposed method correlate well with the results of the indirect and manual evaluations.
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46

Masta, Karan, and Yasmin Janjhua. "Training Evaluation Models for Farmer Training Programmes." International Journal of Economic Plants 7, no. 3 (August 28, 2020): 115–18. http://dx.doi.org/10.23910/2/2020.0374.

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Training has been an effective means to attain knowledge, skill and abilities adding to human efficiency and effectiveness. Ensuring effective training means knowing whether investment of time, energy and resources are being spent effectively or not. Active play of diverse resources makes it imperative to evaluate the effectiveness of the training. The paper attempts to explain the important models of training evaluation which can be adopted by practitioners for evaluating farmer trainings. These approaches can be used as a base to assess farmer training interventions and also help to revise and design an intervention which is effective and free from impediments.
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47

Fabbri, Alexander R., Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. "SummEval: Re-evaluating Summarization Evaluation." Transactions of the Association for Computational Linguistics 9 (2021): 391–409. http://dx.doi.org/10.1162/tacl_a_00373.

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Abstract The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress. We address the existing shortcomings of summarization evaluation methods along five dimensions: 1) we re-evaluate 14 automatic evaluation metrics in a comprehensive and consistent fashion using neural summarization model outputs along with expert and crowd-sourced human annotations; 2) we consistently benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics; 3) we assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset and share it in a unified format; 4) we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics; and 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgments of model-generated summaries on the CNN/Daily Mail dataset annotated by both expert judges and crowd-source workers. We hope that this work will help promote a more complete evaluation protocol for text summarization as well as advance research in developing evaluation metrics that better correlate with human judgments.
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48

Eash, Maurice J. "Evaluation models: Viewpoints on educational and human services evaluation." Evaluation and Program Planning 9, no. 1 (January 1986): 100–102. http://dx.doi.org/10.1016/0149-7189(86)90013-3.

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49

Kaewpet, Chamnong. "Quality of Argumentation Models." Theory and Practice in Language Studies 8, no. 9 (September 1, 2018): 1105. http://dx.doi.org/10.17507/tpls.0809.01.

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This study investigated the effectiveness of updated argumentation quality criteria. It evaluated the scale and quality of selected argumentation models judged by the new criteria. Effectiveness concerned content validity, reliability, and practicality of the criteria. The argumentation models were regarded as possessing good quality when they featured important elements in the criteria and received high scores. Five argumentation models were purposefully selected from an argumentative writing course. The models were evaluated by three evaluators with expertise in academic writing. Analysis confirmed the effectiveness of the quality criteria and scale. It addressed all important concerns in evaluation of argumentation; evaluation scores were in accordance with each other, and the important argumentation elements carried equivalent weight. Only three of the models received a quality score of 4 on a scale of 0 (null) to 5 (highest), because they did not feature all quality elements required by the criteria. The updated framework and argumentation models can be further employed for teaching, learning and evaluating argumentation.
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50

Hutchins, M., K. Urama, E. Penning, J. Icke, C. Dilks, T. Bakken, C. Perrin, T. Saloranta, L. Candela, and J. Kämäri. "The model evaluation tool: guidance for applying benchmark criteria for models to be used in river basin management." River Systems 17, no. 1-2 (July 28, 2006): 23–48. http://dx.doi.org/10.1127/lr/17/2006/23.

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