Is it time for libraries to take a closer look at emoji? The data deluge column
Bibliographic record
Abstract
Purpose The emoji, is it an endearing image to add to your text messages and email, or is it an increasingly important type of electronic data? According to a 2013 article by Jeff Blagdon, the idea of using some sort of symbol in electronic communication has been with us for about two decades. Japanese in origin, the earliest symbols of this type were developed in the era of pagers and old-style cell phones and were commonly called emoticons. Design/methodology/approach As devices developed a greater capacity to display graphical elements these keystroke representations were replaced with Unicode characters which display on our electronic devices, which we now call emoji. This instalment of the data deluge will look at the emoji as a form of data and explore how and why their ubiquity may create new opportunities for libraries. Findings Some readers, as well as the author of this column, may be tempted to scoff at the idea that the emoji is anything more than a form of shorthand for use in electronic communications or cutesy decorations. Originality/value One night she showed up at the class, and the instructor wrote on the board, “Computers in school libraries: A new tool or a flash in the pan?” He went on to warn school librarians to not be dazed by this “new computer phase” which he felt distracted both teachers and students from the real work of teaching and learning. He felt that if there were computers in schools, they only belonged in the mathematics classroom and that, even in that context, they only had limited application.
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 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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.117 | 0.083 |
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".