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Record W3119252148 · doi:10.1080/07294360.2020.1867513

Alternative dissertation formats in education-based doctorates

2021· article· en· W3119252148 on OpenAlexaffabout
Tim Anderson, Gillian Saunders-Smits, Ian Alexander

Bibliographic record

VenueHigher Education Research & Development · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
Fundersnot available
KeywordsPopularityDoctoral dissertationSociologyEducational researchHigher educationPedagogyPsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

The doctorate and doctoral writing remain popular areas of inquiry and discussion, and yet very little research has empirically investigated the trends in dissertation types and how these trends might indicate broader changes in dissertation writing practices. This article builds on our recent work that investigated the macrostructures and research designs of 1,373 education-based PhD dissertations from five major Canadian research universities. In this current article, we more deeply explore the emergence in popularity of two ‘alternative’ or non-traditional dissertation macrostructures in education fields: the manuscript-style dissertation and the topic-based PhD dissertation. We highlight the popularity of these two dissertation types as evidence of shifting notions of what doctoral research and dissertations can (and do) look like in contemporary PhD programs. We focus specifically on these two dissertation macrostructures that were prevalent in our analysis, yet which are scarcely addressed in education-based dissertation resources. We provide a deeper reflection on the popularity of these dissertation models from our large-scale study, the ways these types of dissertations are organized at the global (macrostructural) level, and the chosen research designs, number of chapters, word counts, and authorship status (as either single-authored, partially co-authored, or mostly co-authored texts).

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptScholarly communication
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Other designlow
grokScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
opusScholarly communication
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.069
metaresearch head score (Gemma)0.190
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.190
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.010
Science and technology studies0.0080.007
Scholarly communication0.0150.007
Open science0.0020.012
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.002

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 head0.224
GPT teacher head0.599
Teacher spread0.375 · 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

Labeled directly by 3 models reading the full record.

Study designOther design
Domainnot available
GenreOther · Empirical · Review

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

Citations18
Published2021
Admission routes2
Has abstractyes

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