THE MEANING OF “e-”: NEOLOGISMS AS MARKERS OF CULTURE AND TECHNOLOGY
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
A community is known by the language it keeps, and its words chronicle the times. Every aspect of the life of a people is reflected in the words they use to talk about themselves and the world around them. As their world changes – through invention, discovery, revolution,evolution or personal transformation – so does their language. Like the growth rings of a tree, our vocabulary bears witness to our past.- John Algeo (Fifty Years Among the New Words)Algeo reveals two interesting concepts in this simple passage. First, he acknowledges the intricate relationship between language and culture. Although it is no secret that both language and culture change over time, he explains how language acts as a marker of history, reflecting back culture as it changes. Secondly, he points to vocabulary as the primary indicator for tracking this change and recognizes that new words or neologisms can be useful tools for understanding how culture is evolving. Algeo shows us that through monitoring vocabulary change, we can track cultural change. New words are constantly entering the lexicon to describe new concepts and technologies and what they mean to us. Conversely, older words continually fall out of use as they decrease in cultural significance. Considering the influence digital technology has had on society, it is not surprising then that lexicographers have found that science and technology are by far the most prolific sources of neologisms in recent times (Crystal 2002; Knowles & Elliot 1997;Van Dyke 1992; Gozzi 1990).
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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