| _version_ | 1866902290653249536 |
|---|---|
| author | Dekel, Yael Marienberg-Milikowsky, Itay Jacobson, Gilad Aviel |
| author_facet | Dekel, Yael Marienberg-Milikowsky, Itay Jacobson, Gilad Aviel |
| contents | <p dir="auto">data (EXCEL) and code (Matlab 2024b) for JCLS submission</p> <p dir="auto"><strong>Data</strong> <strong>240813 - Key Novel Dataset - 9 - removed pilot entries.xlsx</strong> This file has been manually pre-processed to remove pilot questionnaires (that were incomplete) and fix other errors such as incorrect spelling of book names and authors. In addition, we manually added a pair of columns (author 2 and gender of author 2) to enable us to deal with books with two authors, in which it was impossible to identify either because they were combined.</p> <p dir="auto"><strong>Data extraction code</strong> <strong>extractTables2.m</strong> This code reads the excel file and generates the necessary data structure in variable D, then saved in the Matlab data file <strong>allData250203.mat</strong></p> <p dir="auto"><strong>fig1_ambigScale.m</strong> Further analysis of scaled items and generation of figures 1A-C.</p> <p dir="auto"><strong>fig2_modesOfAmbivalence.m</strong> Futher analysis of ambivalent items and generation of figures 2A-C.</p> <p dir="auto"><strong>fig3a.m</strong> Analyses whether the identity of the book/title affects the degree of ambivalence reported by readers.</p> <p dir="auto"><strong>fig3b.m</strong> Analyses whether the reader identity affects the degree of ambivalence reported across all questionnaires of that reader.</p> <p dir="auto"><strong>Accessory code</strong></p> <ol> <li> <p dir="auto"><strong>directPoissonBinomial.m</strong> A self-written function to calculate the Poisson-Binomial distribution. Input: p - a vector of n elements containing the probability of success for each Bernoulli trial. k - the number of successful Bernoulli trials (irrespective of position). Output: prob - the probability of achieving exactly k successful trials</p> </li> <li> <p dir="auto"><strong>cellflat</strong> A helper function to flatten nested cell arrays. Input: celllist - a cell array to be flattened n - an optional input, limiting the number of flattened levels to n. Output: out - a flattened cell array</p> </li> </ol> <p dir="auto"><strong>Accessory data structures</strong></p> <p dir="auto">These are used to analyse some of the questionnaire items, by providing a table that allows translation between answer and some number / vector that can be used for analysis. Most of them are irrelevant for the current manuscript, but are necessary for the code to run. Included are:</p> <ol> <li> <p dir="auto"><strong>charNumKey.mat</strong> Used for transforming the verbal answers into a number of main characters (first column) and secondary characters (second columns). Numbers in the range 0-4 should be interpreted literally. 10 encodes "several" and variations thereof, and 100 encodes "many" and variations thereof.</p> </li> <li> <p dir="auto"><strong>defaultCitations.mat</strong> The item asking about sources cited in the novel has both multiple choices and an open field. This data structure contains the pre-set multiple choices.</p> </li> <li> <p dir="auto"><strong>defaultGenres.mat</strong> The item asking about genre types has both multiple choices and an open field. This data structure contains the pre-set multiple choices.</p> </li> <li> <p dir="auto"><strong>evtNumKey.mat</strong> Number of key events in novel. This item contains both pre-set multiple choice and free text, and has to be translated into numbers. As before, 10 denotes "several" and variations thereof, 100 denotes "many" and variations thereof.</p> </li> <li> <p dir="auto"><strong>geoData.mat</strong> Contains a data structure generated manually with all the 63 geographical entities that are given as answers in the item about geographical locations mentioned in the novel. Contains two data structures: geoEntity - a 5x63 cell array, with each column providing the continent/region/country/city/entity corresponding to one possible answer. Region: e.g. SE Asia, W Europe, ... Entity: e.g. military base. If the answer included only a continent, rows 2-5 will be empty. But if only a city was mentioned, the column will contain rows 1-4 and only row 5 will be empty. geoHier - a 9x63 binary matrix. Rows 1-5 indicate whether the entity corresponds to one of the above positions in the hierarchy. Rows 7-9 correspond to the following 3 categories: (7) undefined territory; (8) historic entity (e.g. Babylonian empire); (9) unrealistic entity (e.g. fictional island)</p> </li> <li> <p dir="auto"><strong>importData.mat</strong> Translates multiple choice answers about the impotrance of the novel into binary categories defined by us.</p> </li> <li> <p dir="auto"><strong>languagesUsed.mat</strong> Translates user free text answers into a code that can be analysed.</p> </li> <li> <p dir="auto"><strong>anonID.mat</strong> Contains the anonymised ID of the reader, encoded as a number.</p> </li> </ol> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17253379 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Readers to Data - JCLS 2025 Dekel, Yael Marienberg-Milikowsky, Itay Jacobson, Gilad Aviel <p dir="auto">data (EXCEL) and code (Matlab 2024b) for JCLS submission</p> <p dir="auto"><strong>Data</strong> <strong>240813 - Key Novel Dataset - 9 - removed pilot entries.xlsx</strong> This file has been manually pre-processed to remove pilot questionnaires (that were incomplete) and fix other errors such as incorrect spelling of book names and authors. In addition, we manually added a pair of columns (author 2 and gender of author 2) to enable us to deal with books with two authors, in which it was impossible to identify either because they were combined.</p> <p dir="auto"><strong>Data extraction code</strong> <strong>extractTables2.m</strong> This code reads the excel file and generates the necessary data structure in variable D, then saved in the Matlab data file <strong>allData250203.mat</strong></p> <p dir="auto"><strong>fig1_ambigScale.m</strong> Further analysis of scaled items and generation of figures 1A-C.</p> <p dir="auto"><strong>fig2_modesOfAmbivalence.m</strong> Futher analysis of ambivalent items and generation of figures 2A-C.</p> <p dir="auto"><strong>fig3a.m</strong> Analyses whether the identity of the book/title affects the degree of ambivalence reported by readers.</p> <p dir="auto"><strong>fig3b.m</strong> Analyses whether the reader identity affects the degree of ambivalence reported across all questionnaires of that reader.</p> <p dir="auto"><strong>Accessory code</strong></p> <ol> <li> <p dir="auto"><strong>directPoissonBinomial.m</strong> A self-written function to calculate the Poisson-Binomial distribution. Input: p - a vector of n elements containing the probability of success for each Bernoulli trial. k - the number of successful Bernoulli trials (irrespective of position). Output: prob - the probability of achieving exactly k successful trials</p> </li> <li> <p dir="auto"><strong>cellflat</strong> A helper function to flatten nested cell arrays. Input: celllist - a cell array to be flattened n - an optional input, limiting the number of flattened levels to n. Output: out - a flattened cell array</p> </li> </ol> <p dir="auto"><strong>Accessory data structures</strong></p> <p dir="auto">These are used to analyse some of the questionnaire items, by providing a table that allows translation between answer and some number / vector that can be used for analysis. Most of them are irrelevant for the current manuscript, but are necessary for the code to run. Included are:</p> <ol> <li> <p dir="auto"><strong>charNumKey.mat</strong> Used for transforming the verbal answers into a number of main characters (first column) and secondary characters (second columns). Numbers in the range 0-4 should be interpreted literally. 10 encodes "several" and variations thereof, and 100 encodes "many" and variations thereof.</p> </li> <li> <p dir="auto"><strong>defaultCitations.mat</strong> The item asking about sources cited in the novel has both multiple choices and an open field. This data structure contains the pre-set multiple choices.</p> </li> <li> <p dir="auto"><strong>defaultGenres.mat</strong> The item asking about genre types has both multiple choices and an open field. This data structure contains the pre-set multiple choices.</p> </li> <li> <p dir="auto"><strong>evtNumKey.mat</strong> Number of key events in novel. This item contains both pre-set multiple choice and free text, and has to be translated into numbers. As before, 10 denotes "several" and variations thereof, 100 denotes "many" and variations thereof.</p> </li> <li> <p dir="auto"><strong>geoData.mat</strong> Contains a data structure generated manually with all the 63 geographical entities that are given as answers in the item about geographical locations mentioned in the novel. Contains two data structures: geoEntity - a 5x63 cell array, with each column providing the continent/region/country/city/entity corresponding to one possible answer. Region: e.g. SE Asia, W Europe, ... Entity: e.g. military base. If the answer included only a continent, rows 2-5 will be empty. But if only a city was mentioned, the column will contain rows 1-4 and only row 5 will be empty. geoHier - a 9x63 binary matrix. Rows 1-5 indicate whether the entity corresponds to one of the above positions in the hierarchy. Rows 7-9 correspond to the following 3 categories: (7) undefined territory; (8) historic entity (e.g. Babylonian empire); (9) unrealistic entity (e.g. fictional island)</p> </li> <li> <p dir="auto"><strong>importData.mat</strong> Translates multiple choice answers about the impotrance of the novel into binary categories defined by us.</p> </li> <li> <p dir="auto"><strong>languagesUsed.mat</strong> Translates user free text answers into a code that can be analysed.</p> </li> <li> <p dir="auto"><strong>anonID.mat</strong> Contains the anonymised ID of the reader, encoded as a number.</p> </li> </ol> |
| title | From Readers to Data - JCLS 2025 |
| url | https://doi.org/10.5281/zenodo.17253379 |