<i>“The Future Is Already Here”: Artificial Intelligence as an Exercise in Human Redundancy</i>
Bibliographic record
Abstract
A moment ago, as I started drafting this Editor's note, unhappy with the good old cliché, "to see the light of print," I asked Chat GPT to give me a fresher, more contemporary equivalent of it.It instantly came up with a set of phrases and a sensible rationale: "a more contemporary equivalent for 'to see the light of print' is 'to be published online' or 'to be digitally released'."These reflect the shift from conventional print media to digital platforms in today's publishing landscape.And once I've completed this, I can go into the lecture room and ask ChatGPT to speak for me, in my voice, in a LLD language, i.e. a language of limited diffusion, on a topic of my own choosing.So empowered, I feel as though I "contain multitudes."This edition of American, British and Canadian Studies is 'digitally released' on the cusp of accelerating change.In the ever-evolving landscape of digital humanities, the intersection of literary studies and artificial intelligence (AI) has emerged as a consequential realm of enquiry, one of profound significance.Whereas quite a few vintage litterateurs out there will fail to see how the synergy between these two seemingly disparate domains can enrich one's understanding of literary works, many will ruminate on the compelling questions it raises about the nature of creativity, authorship, authenticity.Ineluctably, one cannot but remain divided between the promising avenues of creative exploration and the ethical concerns that underpin the entwined relationship between literary-linguistic studies, Academia and AI.While Translation and Interpretation Studies have already embraced the change, acting in 'collusion' and compliance with AI, literature has, understandably, held back.In literary studies, this burgeoning, potentially empowering synergy is replete with multiple quandaries and ethical considerations.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".