Replication Data for: Non-standard typography as performative protection: from indexicality to enregisterment.
Bibliographic record
Abstract
Data for a solo-authored paper accepted by editors Florina Zülli, Christa Dürscheid, and Ronja Eggenschwiler. Questionnaire designed to test participant response to and usage of the non-standard lowercase 1SG in English (). In July 2023, 175 participants completed a written questionnaire. It was administered in person to 83 middle school students from Manchester, UK, aged 11–12, and simultaneously distributed online to reach further demographics. All participants were agnostic to the study’s premise. Respondents fell into five age brackets: under 18, 18–30, 30–50, 50–70, and 70+, primarily from the UK, with additional responses from Australia, the USA, and Canada. The questionnaire was comprised of a written stimuli (a forged utterance containing ), and 6 questions about their attitude towards the usage of . There were an additional 3 demographic questions about participant age, sex, and geographic location. Gender data showed that the survey was taken by 111 female and 54 male participants, the remainder preferring not to say.
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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.216 | 0.112 |
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".