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Record W2152972611 · doi:10.5430/wje.v5n4p38

Typical and Individual Doctoral Processes and Lifecourses: The Types of Narratives of the Project Manager, the Survivor and the Seeker

2015· article· en· W2152972611 on OpenAlexvenueno aff
Minna Maunula

Bibliographic record

VenueWorld Journal of Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePsychologyQuality (philosophy)Doctoral studiesDoctoral dissertationTask (project management)Competition (biology)PedagogySocial psychologyHigher educationManagementEpistemologyPolitical scienceEcology

Abstract

fetched live from OpenAlex

One task of the doctoral education is, globally as well as nationally, to produce and renew the highest expertise andknowledge in a high quality and efficient way. Even though in this global time the high-quality knowledge and skillsare a competition factor which the success of the societies is expected to be able to lean on, also the doctoral studentsand their individual factors are significant. The accelerating global change is strongly reflected at the individual level:an attempt is made to respond to the changing expectations and to prepare individually and diversely. The individualdoctoral students and the graduating doctors come from different everyday lives and contexts. The graduatingdoctors' expertise and skills are individually colored during the individual doctoral processes. Often the doctoralstudents' and the graduating doctors' individuality is ignored – even though individuality in other contexts isidentified more clearly than before. In this article I examine the lifecourse experiences and stories ofunder-40-year-old female doctoral students with a family and form three different types of doctoral student on thebasis of the material. The examination concentrates on the areas of the lifecourse; the family, doctoral studies andwork as well as on the dynamic wholeness formed by them in the temporal continuum of the lifecourse. Theobjective is to make the generalized doctoral process more comprehensively intelligible.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.217
GPT teacher head0.492
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2015
Admission routes1
Has abstractyes

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