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Record W2949699556 · doi:10.36615/sotls.v3i1.65

Doctoral supervision in developing countries: desperately seeking the Scholarship of Teaching and Learning

2019· article· en· W2949699556 on OpenAlexaff
Pammla Petrucka

Bibliographic record

VenueScholarship of Teaching and Learning in the South · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsScholarshipLicenseCognitive reframingScholarship of Teaching and LearningDeveloping countryPolitical scienceSociologyHigher educationPublic relationsPedagogyPsychologyEconomic growthTeaching methodEconomicsTeaching and learning centerLawSocial psychology

Abstract

fetched live from OpenAlex

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/

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 imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.011
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.143
GPT teacher head0.449
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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