Thesis Lounge: A graduate student organized session - Remote data collection in a COVID-19 pandemic era: A perspective of a graduate student studying in Canada and collecting his PhD thesis research data in Africa
Bibliographic record
Abstract
Thesis Lounge: a graduate student organized session\nRemote data collection in a COVID-19 pandemic era: A perspective of a graduate student studying in Canada and collecting his PhD thesis research data in Africa.\nRationale: This session is a participatory session with the lead presenter being a current Ph.D. Candidate at Western University in Canada. The presenter shares his remote data collection experience from two African countries during the height of the COVID-19 pandemic. This session begins with a 30 minute presentation designed to describe the presenter’s approach to collecting data remotely, as well as highlighting tips to improve success as well as potential pitfalls to take note of.\nThe next part of the session will build on the presentation and focus on drawing from other participants who have also collected their graduate research data from African countries remotely. Their perspectives and experiences will also enrich the session when they bring a multidisciplinary, multi cultural and contextual content to the discussion. The session will conclude with questions and answers from the audience.\nAt the end of this session, graduate students should be guided in how to overcome distance barriers when successfully planning and remotely collecting data from the African continent during a pandemic.\nPresenter: Uche Ikenyei is a PhD. Candidate, Health Information Sciences (HIS), Western University, Canada. His PhD research focuses on exploring ways of improving developing countries’ health information systems for future infectious disease pandemics.\n*Please note this session will be recorded and posted on this page after the conference.\nMeeting ID: 921 8023 8171
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".