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Female Enrolment in High School Computer Science Courses

2020· book-chapter· en· W3096008397 on OpenAlexaffabout
Steven Paul Floyd

Bibliographic record

VenueAdvances in early childhood and K-12 education · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsWestern University
Fundersnot available
KeywordsDoorsCurriculumGovernment (linguistics)Mathematics educationMedical educationEngineeringPsychologyPedagogyMedicineMechanical engineering

Abstract

fetched live from OpenAlex

Jane Margolis and Allan Fisher's book Unlocking the Clubhouse: Women in Computing presented computer education as a clubhouse for boys that was resulting in women and girls being left out of the computer science (CS) loop. This research reveals that now, almost 20 years later, a number of doors, walls, and windows still inhibit certain students from equal access and participation to the computing clubhouse and provides data from Ontario, Canada indicating that females make up only 26%, 21%, and 15.7% of student enrolled in the Grade 10, Grade 11, and Grade 12 high school courses, respectively. Considering the number of initiatives and money related to expanding CS education, including a revision of high school CS curriculum in Ontario and $60 million of additional CanCodes money provided by the federal government, a better understanding of the underrepresentation of females in high school CS is critical.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.280
Teacher spread0.272 · 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 designTheoretical or conceptual
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
Published2020
Admission routes2
Has abstractyes

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