I have questioned its metrics in the past, but they seem to have gotten a handle on that.
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About the trainer Contact Ann Photo gallery. Four Readability Tests on Four Comic Books is a quantative study on the readability levels of four comic books over a span of 12 issues.
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I performed four readability tests and one interest test on 12 issues of each of four super hero comic books: Wonder Woman , Superman , Fantastic Four , and The Amazing Spider-Man. It was originally done as part of a masters program in librarianship in One additional readability test was added in the update. In addition, the Flesch Reading Interest test was performed on each issue. This book provides a solid, quantitative explanation for the use of comic books in schools and for their existence in libraries.
It also provides the framework for further study on other forms of literature. The result of this study was that these 48 issues resulted in a readability level between 3rd and 12th grades, with an average readability level of 8th grade. It is printed in a color format, which accounts for the cost of the book. It has pages and a full report on the data figures and equations that resulted in the final conclusion.
To access this site, click on the picture of the cover of the book or on the title listed above. New Readability Tests on Four Superheroes in Comic Book Collections will be a quantative study on the readability levels of four superheroes over a span of 12 issues. It is planned for completion sometime after August This study is different from the study see above. It uses computer software to create the readability test results, which will be taken from trade book versions of the comic book issues. So, this study will be replicable and capable of duplication for verification.
The results for these tests will then be compared to the results for the study. This book will provide a solid, quantitative explanation for the use of comic books in schools and for their existence in libraries. Thus, this possible explanation cannot fully account for our findings. Lower readability implies less accessibility, particularly for non-specialists, such as journalists, policy-makers and the wider public.
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Scientific journalism offers a key role in communicating science to the wider public Bubela et al. Further, amidst concerns that modern societies are becoming less stringent with actual truths, replaced with true-sounding 'post-facts' Manjoo, ; Nordenstedt and Rosling, science should be advancing our most accurate knowledge. One way to achieve this is for science to maximize its accessibility to non-specialists. Lower readability is also a problem for specialists Hartley, ; Hartley and Benjamin, ; Hartley, While science is complex, and some jargon is unavoidable Knight, , this does not justify the continuing trend that we have shown.
It is also worth considering the importance of comprehensibility of scientific texts in light of the recent controversy regarding the reproducibility of science Prinz et al. Reproducibility requires that findings can be verified independently. To achieve this, reporting of methods and results must be sufficiently understandable.
Readability formulas are not without their limitations. For example, readability can be affected by text size, line spacing, the use of headers, as well as by the use of visual aids such as tables or graphs, none of which are captured by readability formulas Hartley, ; Badarudeen and Sabharwal, Many semantic properties of texts are overlooked, including the complexity of ideas, the rhetorical structure and the overall coherence of the text Bruce et al. Changing a text solely to improve readability scores does not automatically make a text more understandable Duffy and Kabance, ; Redish, Despite the limitations of readability formulas, our study shows that recent scientific texts are, on average, less readable than older scientific texts.
This trend was not specific to any one field, even though the size of this association varied across fields. Further research should explore possible reasons for these differences, as it may give clues on how to improve readability. For example, the adoption of structured abstracts which are known to assist readability Hartley and Benjamin, ; Hartley, might lead to a less steep decline for some fields. What more can be done to reverse this trend? The emerging field of science communication deals with ways science can effectively communicate ideas to a wider audience Treise and Weigold, ; Nielsen, ; Fischhoff, One suggestion from this field is to create accessible 'lay summaries', which have been implemented by some journals Kuehne and Olden, Others have noted that scientists are increasing their direct communication with the general public through social media Peters et al.
However, while these two suggestions may increase accessibility of scientific results, neither will reverse the readability trend of scientific texts.
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Another proposal is to make scientific communication a necessary part of undergraduate and graduate education Brownell et al. Scientists themselves can estimate their own readability in most word processing software. Further, while some journals aim for high readability, perhaps a more thorough review of article readability should be carried out by journals in the review process. Such an 'r-index' could be considered an asset for those scientists who emphasize clarity in their writing. We aimed to obtain journals from which articles are highly cited from a representative selection of the biomedical and life sciences, as well as from journals which cover all fields of science, which were indexed on PubMed.
There should be more than 15 years between the years of the first five and most recent five PubMed entries. The impact factor of the journal should not be below 1 according to the Thomson Reuters Journal Citation Report. The number of selected journals should provide as equal representation as possible of subfields within the broader research fields. From each of 11 of the fields, the 12 most highly cited journals were selected.
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The final field Multidisciplinary only contained six journals, as no more journals could be identified which met all inclusion criteria. Some journals exist in multiple fields, thus the number of journals is below the possible maximum of journals. See Supplementary file 1 for the journals and their field mappings.
The later download date was to correct for originally having only included 11 journals in one of the fields when the data was first downloaded. The text of the abstract, journal name, title of article, PubMed IDs and publication year were extracted. Throughout the article, we only used data up to and including the year Abstracts downloaded from PubMed were preprocessed so that the words and syllables could be counted. TreeTagger version 3. Scientific texts contain numerous phrasings which TreeTagger did not parse adequately.
We did three rounds of quality control where at least preprocessed articles, sampled at random, were compared with their original texts. After identifying irregularities with the TreeTagger performance, regular expression heuristics were created to prepare the abstracts prior to using the TreeTagger algorithm. After the three rounds of quality control, the stripped abstracts contained only words with at least one syllable and periods to end sentences.
Sentences containing only one word were ignored. The heuristic rules after quality control rounds included: removing all abbreviations, adding spaces after periods when missing, adding a final period at the end of the abstract when missing, removing numbers that ended sentences, identifying sentences that end with 'etc. Examples of texts before and after preprocessing are presented in Supplementary file 3.
We confirmed that the observed trends were not induced by the preprocessing steps by running the readability analysis presented in Figure 1D,E using the raw data Figure 2—figure supplement 1. These measures use different language metrics: syllable count, sentence count, word count and percentage of difficult words. Two different readability metrics were chosen to ensure that the results were not induced by a single method.
NDC was chosen since it is both well established and compares well with more recent methods for analyzing readability Benjamin, Counting the syllables of a word was performed in a three step fashion. First, the word was required to have a vowel or a 'y' in it. Second, the word was queried against a dictionary that contained specified syllable counts using the natural language toolkit NLTK version 3.
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If there were multiple possible syllable counts for a given word, the longer alternative was chosen. Third, if the word was not in the dictionary, the number of vowels excluding diphthongs was counted. If a word ended in a 'y', this was counted as an additional syllable in this third step.