My Turn: Reading the Agenda of the 2003 Annual AAUP Directors' Meeting to Discover What's Wrong With University Press Publishing
Bibliographic record
Abstract
One of the hardest tasks for my authors is to step out of their disciplinary isolation and give me a one-liner about their book that will make sense to the average intelligent reader. It’s not that these bright people can’t accomplish the task, but after having been trained to communicate in highly specific (and therefore often exclusionary) ways, it can be hard for them to surface long enough to see the big picture. So too I believe that university presses have developed ways of communicating among ourselves that reinforce stereotypes about what we do, who our masters are, and where we believe our activity ranks in the ever-shifting sands of university fiscal priorities. A notable example of this is the meticulously crafted agenda for the annual directors’ meeting held in conjunction with the 2003 annual meeting of the Association of American University Presses (AAUP). At the outset, I offer my apologies to those who crafted the document (they do not deserve to have their words picked apart like this!). Admittedly without full regard to the valuable substance of the discussions at the meeting, I hope to draw attention to the way our presumably innocent use of language is working against us in these troubled times. Battle lines are quite literally drawn by the agenda’s title, ‘Tales from the Front Lines.’ This is to be a meeting for beleaguered warriors eager to continue fighting the good fight.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.048 | 0.023 |
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".