From graduate school to the outside world: a graduate student organized session Designing your professional career profile for increased visibility
Bibliographic record
Abstract
From graduate school to the outside world: a graduate student organized session\nDesigning your professional career profile for increased visibility\nPanellists:\n● Prof. Bipasha Baruah (Canada Research Chair in Global Women’s Issues, Western University) and Cam Malthaner (Career Education Coordinator, Western University)\nRationale: The path to obtaining a graduate degree is laden with challenges and opportunities intended to prepare students to be significant contributors to society. How can graduate students take full advantage of the graduate school experience to prepare themselves for a thriving career in academia and beyond. As academic job windows are constricting, how can graduate students present their experiences to reflect the skills that potential employers search for? How do they strategically enhance their visibility to potential employers through various trending professional web pages such as Linkedin? What tools are available and how can students make the best of these options? These and more questions will be addressed in this insightful panel discussion. This panel will include short presentations by the panelists and students will have the opportunity to ask further questions after the presentations. Any questions you have in advance can be sent to Eunice Annan-Aggrey (eannanag@uwo.ca) and Aislinn Adams (aadams59@uwo.ca).\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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.110 | 0.072 |
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".