TC-PUBS: a new tool for reflecting on research in technical communication
Bibliographic record
Abstract
A growing body of research on technical and scientific communication promises to inform workplace communication problems and improve practices. Yet working professionals seldom have the resources to subscribe to all potentially helpful journals or to regularly scan the contents of those journals for relevant articles. To improve access to current research, the Miami University student chapter of the Society for Technical Communication (MU-STC) launched in January 2002 a pilot project for an email list to distribute information about current research in the profession. This list began with the cooperation of the editors of three key journals: IEEE Transactions on Professional Communication, the Journal of Technical Writing and Communication, and Technical Communication. Within the first two months, over 160 individuals from the US, Canada, and Europe had subscribed to the list. The paper describes the purpose and status of this resource and provides information on how to subscribe to TC-PUBS.
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 | 0.027 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.116 | 0.062 |
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".