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Journal articles on the topic 'Relatedness'

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Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Barth, Robert J. "Determining Injury-Relatedness, Work-Relatedness, and Claim-Relatedness." Guides Newsletter 17, no. 3 (May 1, 2012): 1–10. http://dx.doi.org/10.1001/amaguidesnewsletters.2012.mayjun01.

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Abstract The American Medical Association's Guides to the Evaluation of Disease and Injury Causation (Causation) is an important component of the AMA Guides library and delineates a type of evaluation that is distinctly different from a diagnostic evaluation, a treatment planning evaluation, a prognosis evaluation, or an impairment evaluation. Causation provides a protocol for determining whether a clinical presentation, in the context of a legal or administrative claim, may be credibly attributed to a claimed cause. This article presents the evaluation protocol from Causation, provides self-assessment questions (so users can check how well they complied with the protocol), highlights the protocol's value as a model for scientifically credible practice in general, and clarifies that the protocol is relevant to claims that involve issues related to forensic causation. Courts and administrative systems have an extremely unfortunate emphasis on opinions from experts rather than on facts. The protocol from Causation is a good example of how clinicians can focus on facts and avoid surrendering to the court or administrative system's emphasis on opinions. The protocol is standardized, objective, fact-based, and scientifically credible and involves the following: establish a diagnosis; apply relevant findings; obtain and assess evidence of exposure; consider other relevant factors; scrutinize the validity of the evidence; and evaluate results and generate conclusions.

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2

Rakib, Rashadul Hasan, Aminul Islam, and Evangelos Milios. "Improving text relatedness by incorporating phrase relatedness with word relatedness." Computational Intelligence 34, no. 3 (January 5, 2018): 939–66. http://dx.doi.org/10.1111/coin.12152.

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3

Betz, Cecily Lynn. "Relatedness." Journal of Pediatric Nursing 19, no. 4 (August 2004): 235–37. http://dx.doi.org/10.1016/j.pedn.2004.06.002.

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4

Keipi, Teo. "Relatedness Online." YOUNG 26, no. 2 (September 24, 2017): 91–107. http://dx.doi.org/10.1177/1103308817715142.

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5

Greaves, William W., Rajiv Das, Judith Green McKenzie, Donald C. Sinclair, and Kurt T. Hegmann. "Work-Relatedness." Journal of Occupational and Environmental Medicine 60, no. 12 (December 2018): e640-e646. http://dx.doi.org/10.1097/jom.0000000000001492.

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6

Culley, Lorraine, and Nicky Hudson. "Constructing Relatedness." Current Sociology 57, no. 2 (March 2009): 249–67. http://dx.doi.org/10.1177/0011392108099165.

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7

Johnson, J. A. "Semantic relatedness." Computers & Mathematics with Applications 29, no. 5 (March 1995): 51–63. http://dx.doi.org/10.1016/0898-1221(94)00248-j.

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8

Crary, Pamela. "Relatedness Matters." Holistic Nursing Practice 30, no. 6 (2016): 345–50. http://dx.doi.org/10.1097/hnp.0000000000000177.

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9

Dujovne, Beatriz E. "Perverse relatedness." Psychoanalytic Psychology 19, no. 3 (2002): 525–39. http://dx.doi.org/10.1037/0736-9735.19.3.525.

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10

Milne, Leah. "Planetary Relatedness." American Book Review 36, no. 5 (2015): 7–8. http://dx.doi.org/10.1353/abr.2015.0086.

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11

Fajans, Jane. "Autonomy and Relatedness." Critique of Anthropology 26, no. 1 (March 2006): 103–19. http://dx.doi.org/10.1177/0308275x06061486.

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12

BENNETT, BETH. "Measures of Relatedness." Ethology 74, no. 3 (April 26, 2010): 219–36. http://dx.doi.org/10.1111/j.1439-0310.1987.tb00935.x.

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13

Spolaore, Enrico, and Romain Wacziarg. "War and Relatedness." Review of Economics and Statistics 98, no. 5 (December 2016): 925–39. http://dx.doi.org/10.1162/rest_a_00554.

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14

Dhandapani, Karthik, and Rakesh Basant. "Measuring Institutional Relatedness." Academy of Management Proceedings 2014, no. 1 (January 2014): 15491. http://dx.doi.org/10.5465/ambpp.2014.15491abstract.

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15

Dionne, Shelley D., Jin Akaishi, Xiujian Chen, Alka Gupta, Hiroki Sayama, Francis J. Yammarino, Andra Serban, Chanyu Hao, Hadassah J. Head, and Benjamin James Bush. "Retrospective Relatedness Reconstruction." Organizational Research Methods 15, no. 4 (May 2, 2012): 663–92. http://dx.doi.org/10.1177/1094428112442572.

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16

Stern, Donnel B. "States of Relatedness." Contemporary Psychoanalysis 42, no. 4 (October 2006): 565–76. http://dx.doi.org/10.1080/00107530.2006.10747129.

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17

Pehrsson, Anders. "Business relatedness measurements." European Business Review 18, no. 5 (September 2006): 350–63. http://dx.doi.org/10.1108/09555340610686949.

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18

Hardenberg, Roland. "Categories of relatedness." Contributions to Indian Sociology 43, no. 1 (February 2009): 61–87. http://dx.doi.org/10.1177/006996670904300103.

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19

Nash, Catherine. "Geographies of relatedness." Transactions of the Institute of British Geographers 30, no. 4 (December 2005): 449–62. http://dx.doi.org/10.1111/j.1475-5661.2005.00178.x.

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20

KHORSI, AHMED. "On morphological relatedness." Natural Language Engineering 19, no. 4 (February 10, 2012): 537–55. http://dx.doi.org/10.1017/s1351324912000071.

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AbstractIn this paper, we discuss the results of a new unsupervised and computationally lightweight scoring of how two words are morphologically related to each other. This measure is meant to be an alternative to stemming, radicals (root) extraction, and morphological analysis in a wide range of applications; especially information extraction related ones. Compared to light stemming, which seems to be the most convenient approach for systems with efficiency concerns, our measure does not neglect unconditionally a prefix or a suffix as the light stemming does. Instead, our measure takes into account all letters of the word but with different weights. This prevents the missing of a significant letter. Compared to heavy stemming, morphological analysis, or radicals extraction, which rely on dictionaries and compatibility databases, our measure does not rely on any language-specific morphology knowledge. This makes our approach unsupervised and theoretically language independent and computationally much lighter. Our tests targeted Arabic: a Semitic language recognized to have a complex morphology due to its highly inflectional lexicon.

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21

Cole, B. J. "Expected Relatedness: Correction." Science 286, no. 5437 (October 1, 1999): 49f—49. http://dx.doi.org/10.1126/science.286.5437.49f.

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22

Thompson, Emma L., Catherine M. S. Plowright, Cristina M. Atance, and Julian S. Caza. "Reasoning and relatedness." Evolution and Human Behavior 36, no. 1 (January 2015): 38–43. http://dx.doi.org/10.1016/j.evolhumbehav.2014.08.006.

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23

Fitjar, Rune Dahl, and Bram Timmermans. "Relatedness and the Resource Curse: Is There a Liability of Relatedness?" Economic Geography 95, no. 3 (January 24, 2019): 231–55. http://dx.doi.org/10.1080/00130095.2018.1544460.

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24

Nocker, Elisabeth, Harry P. Bowen, Kurt Matzler, and Christian Stadler. "The Impact of Technological Relatedness and Customer Relatedness on Firm Performance." Academy of Management Proceedings 2012, no. 1 (July 2012): 14020. http://dx.doi.org/10.5465/ambpp.2012.14020abstract.

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25

Pamilo, Pekka. "Comparison of Relatedness Estimators." Evolution 44, no. 5 (August 1990): 1378. http://dx.doi.org/10.2307/2409297.

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26

Cutlip, Donald E., Roxana Mehran, Ernest Spitzer, Marie-Claude Morice, and Mitchell W. Krucoff. "Device and Procedure Relatedness." JACC: Cardiovascular Interventions 15, no. 7 (April 2022): 783–88. http://dx.doi.org/10.1016/j.jcin.2022.01.021.

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27

Leverant, Robert. "It's All About Relatedness." Psychological Perspectives 49, no. 2 (December 2006): 306. http://dx.doi.org/10.1080/00332920600998726.

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28

Rodríguez-Consuegra, Francisco. "Bradley, Frege and Relatedness." Bradley Studies 5, no. 2 (1999): 113–25. http://dx.doi.org/10.5840/bradley1999527.

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29

Fathalla, Said. "Detecting Human Diseases Relatedness." International Journal on Semantic Web and Information Systems 14, no. 3 (July 2018): 120–33. http://dx.doi.org/10.4018/ijswis.2018070106.

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Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

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30

Wittine, Bryan. "Relatedness in Analytical Psychotherapy." Jung Journal 6, no. 3 (July 2012): 75–79. http://dx.doi.org/10.1525/jung.2012.6.3.75.

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31

Nisbet, Elizabeth K., John M. Zelenski, and Steven A. Murphy. "The Nature Relatedness Scale." Environment and Behavior 41, no. 5 (August 2008): 715–40. http://dx.doi.org/10.1177/0013916508318748.

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32

Hall, Kathleen Currie, Claire Allen, Tess Fairburn, Michael Fry, Michael Mcauliffe, and Kevin McMullin. "Measuring perceived morphological relatedness." Canadian Journal of Linguistics / La revue canadienne de linguistique 61, no. 1 (2016): 31–67. http://dx.doi.org/10.1353/cjl.2016.0007.

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33

Biancoli, Romano. "Individuation in Analytic Relatedness." Contemporary Psychoanalysis 38, no. 4 (October 2002): 589–612. http://dx.doi.org/10.1080/00107530.2002.10747187.

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34

Warin, Megan J. "Reconfiguring Relatedness in Anorexia." Anthropology & Medicine 13, no. 1 (April 2006): 41–54. http://dx.doi.org/10.1080/13648470500516147.

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35

Galván-Femenía, Iván, Jan Graffelman, and Carles Barceló-i-Vidal. "Graphics for relatedness research." Molecular Ecology Resources 17, no. 6 (May 12, 2017): 1271–82. http://dx.doi.org/10.1111/1755-0998.12674.

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36

Hobson, R. Peter. "Emotion as Personal Relatedness." Emotion Review 4, no. 2 (April 2012): 169–75. http://dx.doi.org/10.1177/1754073911430141.

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37

Hall, Kathleen Currie, Claire Allen, Tess Fairburn, Michael Fry, Michael McAuliffe, and Kevin McMullin. "Measuring perceived morphological relatedness." Canadian Journal of Linguistics/Revue canadienne de linguistique 61, no. 1 (March 2016): 31–67. http://dx.doi.org/10.1017/cnj.2016.2.

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AbstractThis paper provides a metric for determining whether a given pair of English words is perceived to be morphologically related, based on objective measurements of the words’ orthographic, phonetic, and semantic similarity to each other. The metric is developed on the basis of results from a behavioural study in which participants were asked to judge the relative similarity of pairs of words. The metric is intended to help researchers determine which forms in a language plausibly have segments that alternate; as an example, it is applied to the lexicon of English to illustrate its utility in calculating the frequency of alternation of [s] and [ʃ].

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38

Scheffrahn, Wolfgang, and Nélly Ménard. "Ecology, demography and relatedness." Primates 34, no. 3 (July 1993): 413–15. http://dx.doi.org/10.1007/bf02382637.

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39

Pamilo, Pekka. "COMPARISON OF RELATEDNESS ESTIMATORS." Evolution 44, no. 5 (August 1990): 1378–82. http://dx.doi.org/10.1111/j.1558-5646.1990.tb05240.x.

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40

Woodward, Richard. "Worldmates and internal relatedness." Philosophical Studies 166, no. 2 (November 1, 2012): 419–27. http://dx.doi.org/10.1007/s11098-012-0027-0.

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41

CHAKRABARTI, S. K., A. K. HALDER, and P. K. CHAKRABARTTY. "Relatedness among Rhizobium species." Journal of General and Applied Microbiology 36, no. 1 (1990): 47–54. http://dx.doi.org/10.2323/jgam.36.47.

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42

LEE, GWENDOLYN K., TIMOTHY B. FOLTA, and MARVIN B. LIEBERMAN. "RELATEDNESS AND MARKET EXIT." Academy of Management Proceedings 2010, no. 1 (August 2010): 1–6. http://dx.doi.org/10.5465/ambpp.2010.54493686.

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43

Bograd, Michele. "Enmeshment, Fusion or Relatedness?" Journal of Psychotherapy & The Family 3, no. 4 (March 30, 1988): 65–80. http://dx.doi.org/10.1300/j287v03n04_05.

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44

Trevarthen, Colwyn. "Retrieving Relatedness in Psychotherapy." Contemporary Psychology 47, no. 6 (December 2002): 779–82. http://dx.doi.org/10.1037/001308.

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45

Blum, Lawrence D. "Music, Memory, and Relatedness." International Journal of Applied Psychoanalytic Studies 10, no. 2 (June 2013): 121–31. http://dx.doi.org/10.1002/aps.1354.

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46

KONOVALOV, DMITRY A., and DIK HEG. "TECHNICAL ADVANCES: A maximum-likelihood relatedness estimator allowing for negative relatedness values." Molecular Ecology Resources 8, no. 2 (June 28, 2008): 256–63. http://dx.doi.org/10.1111/j.1471-8286.2007.01940.x.

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47

Tsatsaronis, G., I. Varlamis, and M. Vazirgiannis. "Text Relatedness Based on a Word Thesaurus." Journal of Artificial Intelligence Research 37 (January 25, 2010): 1–39. http://dx.doi.org/10.1613/jair.2880.

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The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.

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48

Wei, Tingting, and Huiyou Chang. "Measuring Word Semantic Relatedness Using WordNet-Based Approach." Journal of Computers 10, no. 4 (2015): 252–59. http://dx.doi.org/10.17706/jcp.10.4.252-259.

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49

Prasetyo, Dimas Teguh, Ratna Djuwita, and Amarina Ariyanto. "Who is more related to the nature? A study from Indonesia." E3S Web of Conferences 74 (2018): 08009. http://dx.doi.org/10.1051/e3sconf/20187408009.

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Previous research had shown that current generation had lower relatedness to nature, but these findings came from studies with mostly western respondents. This study aims to explore-nature relatedness in Indonesian students and to identified what factors are related to their nature relatedness. 363 students from several universities in Indonesia joined the online survey. They were between 17- 43 years old. In our study, we found that Indonesia students were moderately in nature relatedness (M= 85,73, SD= 12,137). Nature relatedness was not related to age, gender, home town, vehicles used for transportation, and time used for smartphones. The major findings of this study have shown that students who are environmental activist were the most related to nature. To enhance nature relatedness, we suggests that joining environmental activities is a good way to promote nature relatedness.

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50

Berg, Elena C., Martin I. Lind, Shannon Monahan, Sophie Bricout, and Alexei A. Maklakov. "Kin but less than kind: within-group male relatedness does not increase female fitness in seed beetles." Proceedings of the Royal Society B: Biological Sciences 286, no. 1910 (September 11, 2019): 20191664. http://dx.doi.org/10.1098/rspb.2019.1664.

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Theory maintains within-group male relatedness can mediate sexual conflict by reducing male–male competition and collateral harm to females. We tested whether male relatedness can lessen female harm in the seed beetle Callosobruchus maculatus . Male relatedness did not influence female lifetime reproductive success or individual fitness across two different ecologically relevant scenarios of mating competition. However, male relatedness marginally improved female survival. Because male relatedness improved female survival in late life when C. maculatus females are no longer producing offspring, our results do not provide support for the role of within-group male relatedness in mediating sexual conflict. The fact that male relatedness improves the post-reproductive part of the female life cycle strongly suggests that the effect is non-adaptive. We discuss adaptive and non-adaptive mechanisms that could result in reduced female harm in this and previous studies, and suggest that cognitive error is a likely explanation.

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