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Record W1977083612 · doi:10.1177/0306312703336004

Preparing the Next Generation of Scientists

2003· article· en· W1977083612 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Studies of Science · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsProcess (computing)Graduate studentsMathematics educationSociologyPedagogyPsychologyComputer science

Abstract

fetched live from OpenAlex

The present paper examines aspects of how students are trained to be scientists during their years in graduate school. The data were collected through open-ended interviews with academic research scientists, and the framework for analysis is provided by a generic social process scheme. My objective is to demonstrate how the social process of managing students is integral to our understanding of the day-to-day activities of scientists. Among the findings is the notion that what is formally taught and written down is not as significant as those things that the students learn through doing and participating in formal and informal interaction with senior students and faculty. The data also appear to suggest that any notion we might have of the rigid and prescribed nature of graduate science education does not match what actually takes place. Rather, the successful completion of research projects and the transition from student to scientist emerges through social interaction that reflects individual differences and the circumstances arising in particular situations and 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.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.005
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.404
GPT teacher head0.514
Teacher spread0.110 · 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