Non-Standard Typography Use Over Time: Signs of a Lack of Literacy or Symbolic Capital?
Bibliographic record
Abstract
New technologies have provoked a debate regarding the role of non-standard typography (e.g. !!!, :-*). Some contend that new technologies undermine literacy while others state that new technologies provide new spaces for expressive writing and signal a form of symbolic capital. While previous research has primarily focused on age and gender to account for non-standard typography, we analyze socio-economic variables – education and income level and the use of NST over time. This study entertains these two competing hypotheses by analyzing non-standard typography in text message exchanges over three and a half months in an underprivileged population: people living in an urban public housing. Data reveal that, within this sample, use of NST increased over time and participants with higher education levels were more likely to use non-standard typography than less educated counterparts. Experience with texting was found to mediate this effect. Findings support a symbolic capital hypothesis of non-standard typography use, suggesting NST is not associated with stigmatizing lack of knowledge or literacy, but rather may signal the knowledge of discourse norms ascribed to texting in a community.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".