Reading among Facebook users
Bibliographic record
Abstract
Recent research on reading situation in Poland indicates decreasing interest in books among adults. This tendency is also supposedly followed by changes in reading models as well as different ways of reader-text interactions. Since internauts are generally believed to be a non-reading community, an online questionnaire for Facebook users was prepared, a group which is quite easy to reach, to get a better insight into the situation. The research was carried out between April 23rd and May 3rd 2011 on a group of 150 respondents. It focused on book reading and purchasing intensity, book choices, most commonly used book sources, reading traditional magazines, online magazines, e-books and using digital libraries. It was available in either Polish or English language version. A total of 138 respondents from Poland, USA, UK, Tunisia, Australia, Belgium, Canada, Finland, Germany, Mexico, South Korea and Switzerland took part, one origin unknown. Most of them (74,67%) were people between 17 and 35 years old, the eldest one being 77, the youngest 13. Out of 138 interviewees, 110 were women, over 76% lived in urban areas and declared their education level as higher (57,3%) or secondary (28,7%). The results proved surprisingly optimistic. Only 9 interviewees admitted that they had not read any books in the last 12 months, others read 0–2 or 3–5 books a month. Fantasy, suspense fiction and classical novels were the most popular among them. When asked about favourite titles, respondents listed mainly classics of world literature and popular bestsellers. Reviews as well as recommendations of family members or friends had the greatest influence on their reading choices. Almost 75% of survey participants bought books, usually once a month or once every few months, however, they preferred to borrow them from libraries or friends. Almost half of Polish respondents read e-books and online magazines, although they avoided using digital libraries. They also read traditional print magazines, especially political and social ones, women and popular science magazines. As the results show, reading still remains one of the most popular forms of spending free time for both interviewed Facebook users and other members of their communities. Books are also a popular subject of their conversations and discussions in both real and virtual space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".