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Record W7022527562

Women in Physics

2019· other· en· W7022527562 on OpenAlexaboutno aff

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2019
Typeother
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsChoseSubject (documents)Qualitative researchField (mathematics)Women in science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate why women are highly underrepresented within the field of physics around the world. The study was mainly conducted through qualitative interviews with adolescent girls and it aimed to shelter more than just a local perspective; therefor the interviews were performed in three different schools, in three different countries; one in Bukoba, Tanzania, one in Guelph, Canada, and one in Vellinge, Sweden. This international standpoint provided a profounder understanding to this worldwide concern. There were many similarities between the responses at the three schools, but there were also numerous differences. The girls in Bukoba suggested that women do not have the confidence to study a hard subject like physics, while the girls in Vellinge and Guelph believe that women chose not to study physics due to old stereotypes and prejudgements, and the fact that they do not want to be part of a minority. A portion of the study will reflect on potential solutions to this problematic unbalance, which includes how to inspire more women to enter the field of physics.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0140.007
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0230.003

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 head0.032
GPT teacher head0.297
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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