Bibliographic record
Abstract
It is with great pleasure that I am writing this introduction to this special issue of Stream: Culture/Politics/Technology dedicated to the conference proceedings of the Graduate Masters Sessions (GMS) hosted by the Canadian Communication Association/Association Canadian de Communication (CCA-ACC) at our annual meeting with the Congress of Social Sciences and Humanities at the University of Calgary in 2016. As the former President of the CCA (2014-2016), I worked for several years as a champion of the Graduate Masters Session, seeing them as a vital means of professionalizing young scholars in our discipline. Not only an opportunity for master’s students to “experience” a large conference and develop the skills necessary present their research to a conference audience, the GMS provide early graduate students with an important opportunity to network, build a community, and see how their work participates in a conversation with students and more senior scholars of communication from across Canada. I have been delighted to oversee the GMS sessions over the last few years, in no small part because I, like my colleagues on the Board of the CCA, value that conversation and the critical contributions made at our annual meetings. Sibo Chen, the English Language Graduate Student Representative on the CCA Board (2015-2017), is to be credited with the idea to produce conference proceedings of the GMS as without his focused energy it would never have gotten off the ground. Further thanks must be extended to the Guest Editors for this issue, Philippa Adam, Chris Chapman, and Dugan Nichols of Simon Fraser University, for their work in cultivating the four papers that appear here. Their work has undoubtedly contributed greatly to the further professionalization of the contributors as they embark on extending the dissemination of their research through publication.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.030 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.102 | 0.059 |
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".