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Record W2901791314 · doi:10.3138/cpp.2017-073

Underrepresentation of Women in Canada’s Information and Communication Technology Sector: What Can We Learn from a Canadian Survey of Adult Skills?

2018· article· en· W2901791314 on OpenAlexaffvenueabout
Richard Mueller, N.T. Khuong Truong, Wynonna Smoke

Bibliographic record

VenueCanadian Public Policy · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsImpactMcMaster UniversityUniversity of Lethbridge
Fundersnot available
KeywordsInformation and Communications TechnologyEconomic shortageProxy (statistics)WagePhenomenonDemographic economicsBusinessLabour economicsPolitical scienceEconomicsComputer scienceGovernment (linguistics)

Abstract

fetched live from OpenAlex

The proportion of women in Canada’s information and communication technology (ICT) sector is well below their proportion in other fields. One hypothesis for this phenomenon is that women may not have the “right stuff” to be heavily involved in ICT. We use basic ICT scores derived from Statistics Canada’s 2012 Survey of Adult Skills, which is the Canadian portion of the 2012 Organisation for Economic Co-operation and Development Programme for the International Assessment of Adult Competencies as a proxy for the “right stuff.” We find that, after controlling for appropriate covariates, Canadian women score higher than men on basic ICT skills. However, women with the same ICT test scores are much less likely than men to be employed in ICT occupations. We also find that hourly wages in ICT occupations are lower for women, but this wage gap is no greater than that in the general labour market. Given the current and projected shortages of ICT professionals, women represent a large, yet untapped, pool of talent for this sector.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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 designObservational
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

Citations10
Published2018
Admission routes3
Has abstractyes

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