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Record W2921290849 · doi:10.1080/03057925.2019.1585229

What sustains doctoral students’ interest? Comparison of Finnish, UK and Spanish doctoral students’ perceptions

2019· article· en· W2921290849 on OpenAlexaff
Kirsi Pyhältö, Jouni Peltonen, Montserrat Castelló, Lynn McAlpine

Bibliographic record

VenueCompare A Journal of Comparative and International Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsMcGill University
Fundersnot available
KeywordsCynicismContext (archaeology)PerceptionVariation (astronomy)Abandonment (legal)PsychologyPolitical scienceSocial psychologyGeography

Abstract

fetched live from OpenAlex

Interest plays a major role in the doctoral experience. However, previous research has not considered how the national context might influence interest. This study focused on exploring cross-national variation in doctoral students’ experiences by comparing Finnish, UK and Spanish doctoral students’ research interests. Participants (n = 2.426) responded to the Doctoral Experience survey. Spanish students sustained higher levels of researcher and instrumental interest compared to both UK and Finnish students. Finnish students displayed the lowest levels of instrumental interest while UK students combined the lowest level of development interest with the highest level of cynicism. Interest was determinant for experienced exhaustion, cynicism, study satisfaction and reducing risk of abandonment across the three contexts. Results suggest that national differences in labour market, career expectations or programme structure can be powerful enough to overcome the incredible variation that has been proven to exist at the more local levels of doctoral nested contexts.

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 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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.358
GPT teacher head0.603
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainIncentives
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

Citations20
Published2019
Admission routes1
Has abstractyes

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