An Exploratory Study on Academic Reading Contexts, Technology, and Strategies
Bibliographic record
Abstract
Reading is a fundamental activity to academic performance at all levels. People’s ability to read efficiently in academic contexts is affected by many factors, including their environment and the technology that they use. In past decades the media that people use to read as well as their reading environments have changed substantially; e.g., people now can use smart phones to read academic material and they are often distracted by notifications. Better reading can potentially be supported through new technologies and the re-design of existing technologies which play a role in the process, but to embark on such design improvements we firstly need to understand the current practices, technologies, preferences, and environments that people use for reading. We present an exploratory study from a survey of 110 participants, offering an updated picture of their reading technology use, environments and strategies. Amongst our main contribution are the results of our analysis, which show that despite a generally negative attitude towards the ability of digital technologies to support focus, there is a pervasive use of technologies in many forms. We also identified that there is a relation between people’s awareness of internal interruptions and their understanding of the negative effect of digital technologies in their attention span. We believe that these results are informative for the design and introduction of new technologies that will support future academic reading endeavours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".