Bibliographic record
Abstract
Damian Willcox (Calgary) and Eric Dyck (Lethbridge) have tenaciously been making comics in Alberta over the last twenty years, largely in the absence of local peers. Their work has helped to set the standard for Alberta comic creators, including myself. The comic-making journeys of Damian and Eric offer a sense of the stories that have contributed to Alberta’s emerging indie scene in 2019.At a time in which comics are increasingly present in mainstream society, and in which the value of visual literacy in general seems to be on the rise in Alberta, the work of these two established local cartoonists is well-positioned to reach new audiences. Damian and Eric are drawing upon their years of experience to educate and engage comics newcomers (both makers and consumers), in their respective communities. Their bodies of work, along with their enthusiastic advocacy for independent comics inspired by lived experience, continue to contribute to the province’s increasingly complex, diverse, and exciting indie ‘scene.’In support of Eric and Damian’s commitment to their chosen medium, this article has been written as a comic.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.053 | 0.023 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".