<i>American Speech</i> : The Next 100 Years
Bibliographic record
Abstract
This issue of American Speech represents the beginning of a new era.With a modern design and a pair of new editors, the journal heads into the next 100 years with fresh excitement and with a vision for the future that builds on a storied past.We thought that the best way into that new future would be to introduce ourselves to you individually, then provide our reflections on the past and our dreams for the future. IntroductionsDerek I've been a member of the American Speech and American Dialect Society (ADS) community since 2008.That January, I presented with my then-M.A. supervisor, Sali Tagliamonte, at the annual meeting held in Chicago.The topic was general extenders (like and stuff and and things like that) in Toronto English (a topic that I later returned to with Alex D' Arcy in our 2019 American Speech paper on Canadian English homogeneity).That same year, American Speech published my first journal article, again with Sali but on the hot topic of the day: the language of instant messaging.For most of the process, I was an undergraduate student, and Sali handled the communication with the journal and plans for revision, but even with the initial reviseand-resubmit response from Michael Adams, I remember feeling like the process was valuable and our paper was going to be all the better for the reviews we received and Michael's editorial direction.Overall, I couldn't have asked for a better introduction into academic publishing.Over the years, I've regularly attended and presented at ADS meetings (eventually bringing my own undergrad and grad students with me) and have published what I think is some of my most important work in the pages of American Speech.In one case, I really got the sense that Tom Purnell went to bat for my coauthor and me in the face of a particularly critical review.This moment of editorial agency always stuck with me and I appreciate it even more today, not just because he eventually published our piece, but because he modelled what it means to be a good editor for me.When Michael Adams first reached out to me in summer 2024 about the possibility of taking on the editor role, I was surprised to say the least.But as I floated the idea around with my family, colleagues, and mentors, I realized that I have always enjoyed the peer review process; recently I had the pleasure of coediting
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.087 | 0.063 |
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".