Is digital always better? Comparing two English print dictionaries with their digital counterparts
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.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".