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Record W3152880280 · doi:10.24908/iqurcp.9915

19. The Experiences of Women in a Biochemistry Education and in Scientific Research Labs at Queen’s University and Durham University

2018· article· en· W3152880280 on OpenAlexvenueaboutno aff
Carly Winters

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsQueen (butterfly)Medical educationUndergraduate researchFace (sociological concept)Graduate studentsPosition (finance)SociologyPsychologyEngineering ethicsMedicineEngineeringSocial scienceBiology

Abstract

fetched live from OpenAlex

This study aims to determine the involvement of women in biochemistry undergraduate, graduate, and professor positions within the biochemistry faculties at Queen’s University, Canada, and Durham University, England. Additionally, the experiences of these women will be shared through their personal accounts of becoming involved in the sciences, their career plans, and whether or not they would recommend the biochemistry program to others and their reasoning for this decision. Most importantly, these students will share their opinions on the involvement of females working in scientific research labs, whether or not they would pursue a full-time position running a scientific research labs, and what barriers they think exist in achieving the full participation of women in biochemistry degrees and academic research jobs. Survey trends will be shared through participant narratives. Exposing the thoughts and perceived issues that these women face is the first step to improving the biochemistry educational system and the academic research workplaces.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0210.009
Scholarly communication0.0100.003
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.162
GPT teacher head0.431
Teacher spread0.270 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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