Bibliographic record
Abstract
I am thrilled to be writing 2018’s Year-End Editorial for CJSDW/R. One of the (many) benefits to working on an ongoing open access journal is that the editorial occurs after the volume is complete. This allows for a review of the year that considers how the published pieces connect to one another. This year we published a piece from the University of Toronto featuring a trialogue on editing pluriligual scholars’ work at the graduate level between James Corcoran, Antoinette Gagné, and Megan McIntosh. Their conversation argues for “flexible, targeted writing support that challenges narrow epistemologies and stale ideologies regarding taboo editing practices of academic and language literacy brokers involved in the production and revision of thesis writing” (p. 1). This piece really frames the two special sections produced this year in our journal which both take on the question of writing in the university, challenging the conventional practices and arguing for flexible and creative solutions.
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 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.187 | 0.127 |
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".