Is digital always better? Comparing two English print dictionaries with their digital counterparts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this paper we discuss advantages and disadvantages of e-dictionaries over print dictionaries in order to answer one increasingly relevant question: is digital always better? We compare the e-content from Oxford University Press and Merriam-Webster flagship dictionaries against their most recent print counterparts. The resulting data shows that the move from print to digital, against popular perception, results in a loss of lexicographical detail and scope. After assessing the user-friendliness of the e-dictionaries’ sites in both desktop and mobile app formats, we conclude that Merriam-Webster currently utilizes the digital medium somewhat better, while Oxford University Press is the current market leader in collaborations with tech giants such as Google. Most crucially, however, both companies have yet to devise and implement optimal ways to balance advertising noise and lexicographical content. Finally, we compare the virtual popularity of e-dictionaries according to their social media efforts and product partnerships. The greatest problem e-dictionaries currently face is that content does routinely change in unspecified and even undocumented ways. Despite these significant disadvantages, the convenience of mobile online accessibility appears to outweigh the concern with the reliability and quality of content.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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 it