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Record W4415741689 · doi:10.1162/99608f92.dbc60044

AI and Writing in the Straddle Generation

2025· article· en· W4415741689 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHarvard Data Science Review · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCybernetics and Technology in Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsColumn (typography)StraddleFace (sociological concept)Technical writingAffect (linguistics)

Abstract

fetched live from OpenAlex

We've read a lot lately about how artificial intelligence (AI) is changing the face of higher education.In this column, Benjamin Outar, an undergraduate student at the University of Toronto, explores how the advent of modern AI tools such as ChatGPT collided with the COVID-19 pandemic and the temporary switch to remote schooling, specifically to affect student writing.Defining his cohort as a "straddle generation" who learned the basics of writing prior to the generative AI explosion, he contrasts their attitudes towards writing with those of students just a couple of years younger-a generational shift in thinking.How has AI affected your schooling and your attitudes?Consider writing a column for Minding the Future and share your experiences.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.348
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it