Foreword [to the Special Issue: Doctoral Seminar 2017 – An International Journey]
Bibliographic record
Abstract
[Extract:] Welcome readers to the Emerging Perspectives Special Issue on the International Doctoral Seminar. The International Doctoral Seminar (IDS) is a collaborative project involving three universities, one each in Australia (Queensland University of Technology—QUT), Canada (University of Calgary—UC), and China (Beijing Normal University—BNU). This Special Issue idea began serendipitously on a beautiful evening in Brisbane after a meal of delicious Vietnamese and Chinese food, shared amongst the doctoral seminar participants during the 2017 cycle, at which I was a student participant. We were standing outside the restaurant chatting and saying goodnight when one of the faculty mentors and one of the student participants approached me. They told me that they had been discussing publishing opportunities for international graduate students. The faculty mentor knew I was one of the editors of EPIGREP and exclaimed, “wouldn’t it be neat to publish a Special Issue of our IDS 2017 experience?” We continued to talk about the possibility and let it sit. The next day, we headed to a market and beach. While we were loading the bus, the faculty mentor asked me to pick up the microphone and introduce EPIGREP to everyone, where I asked if they would be interested in collaborating for a Special Issue. Thinking back, the ride was bumpy, and I had to think organically to represent our journal positively. There were some questions and excitement in the air, despite it being a long and somewhat tiring day already.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.352 | 0.322 |
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".