Academic Publishing 101: How to Share your Research with Wider Audiences, a graduate student organized session
Bibliographic record
Abstract
Academic Publishing 101: How to Share your Research with Wider Audiences\nRationale: While writing your dissertation can sometimes seem like an isolating individual experience, sharing your work with broader audiences can be a way of affirming your relationships to a broader scholarly community as well as the broader public. In this session, we will discuss how to navigate the world of academic publishing—how to select publication forums that are a good fit with your research and how to shape your dissertation writing into scholarly publications and shorter journalistic pieces. This session will be interactive. It will feature a formal presentation as well as a question and answer period.\nSpeaker: Pauline Wakeham is an Associate Professor of Indigenous and Canadian literary and cultural studies. She is also currently the Graduate Development and Professionalization coordinator for the Department of English and Writing Studies at Western—a role in which she helps mentor graduate students in many aspects of professional development.\n*Please note this session will be recorded and posted on this page after the conference.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.329 | 0.256 |
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".