Doctoral supervision in developing countries: desperately seeking the Scholarship of Teaching and Learning
Bibliographic record
Abstract
This reflective paper presents a contextual overview of doctoral supervision in low- and middle-income countries. It highlights several models or frameworks used in Western academic settings. Through a critical lens it considers a number of the opportunities and gaps, which may reframe and/or reform doctoral supervision in the low- and middle-income settings. It identifies a significant gap in the evidence and scholarship on the topic of graduate supervision in developing contexts. In the current and evolving higher education milieu and the global emergence of the knowledge economy, the topic of graduate supervision can no longer go without a serious and fulsome discussion. How to cite this reflective piece: PETRUCKA, Pammla. Doctoral supervision in developing countries: desperately seeking the Scholarship of Teaching and Learning. Scholarship of Teaching and Learning in the South. v. 3, n. 1, p. 92-99, Apr. 2019. Available at: https://sotl-south-journal.net/?journal=sotls&page=article&op=view&path%5B%5D=65&path%5B%5D=41 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".