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Record W3137205379 · doi:10.29173/eureka28750

From the desk of Dr. Jennifer Passey

2020· article· en· W3137205379 on OpenAlexaffvenue
Jennifer Passey

Bibliographic record

VenueEureka · 2020
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of Alberta
FundersEurostars
KeywordsDeskHistoryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

professor of psychology at the University of Delaware, is the author of an innovative and engaging textbook on research methodology.Morling (2018) makes a distinction between the roles of research consumers and research producers.Undergraduate science students are research consumers; they learn and read about research to understand the topics, phenomena, and methodologies within their disciplines.For example, psychology students may learn about how autobiographical memory works, how we pay attention to social information, how cultural worldviews help people manage anxiety about death, or what neuroimaging tells us about the visual attention system.Undergraduate science students are also research producers.Through their coursework, labs, independent studies, honors theses, and other research opportunities, they frequently develop new research questions, and conduct their own investigations to answer those questions.For example, some psychology students engage in research investigating the socialization of cultural practices, or how decision-making changes as a function environment.Others many conduct research on the development of executive function in childhood, or how learning and cognitive abilities allow animals to solve problems they face in the wild.Of course, there are connections between the consumer and producer roles: both share a desire for knowledge, and a faith in empiricism (Morling, 2018).In addition, when developing new research questions, hypotheses, and methodologies, research producers routinely review prior research to inform their own investigations.

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)0.000
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.574
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.269
GPT teacher head0.441
Teacher spread0.171 · 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 designNot applicable
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

Citations0
Published2020
Admission routes2
Has abstractyes

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