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Record W2971231257 · doi:10.11575/prism/36903

Women in the Office: MP Staff in Canada

2019· dissertation· en· W2971231257 on OpenAlexaboutno aff
Meagan Nicole Cloutier

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

In Canadian political science, Member of Parliaments’ (MP) staff are rarely studied. When mentioned, research only examines staff in relation to MPs’ interaction with their constituency office. As an understudied, poorly understood group, this thesis investigates who works for an MP and why they do so. I argue MP staff are important to study due to their direct interaction with constituents dealing with federal government issues. Using two unique datasets, – the Government Electronic Directory Services dataset and data collected in November and December 2018 from a survey of MPs’ employees across Canada - this study addresses four main research questions: who works for an MP and why; who uses their staff position to advance their political ambition; what are the main benefits and drawbacks for working for an MP; and how are these processes gendered? Results show that across Canada, regardless of political party, the gender of the MP and region, more women are employed by MPs than men. Helping constituents and their communities are rewarding aspects of working for an MP, though women report different, more rational motivations for their work than do men. Staff report poor office management, long hours, and low compensation as consistent drawbacks. Approximately one in four staff experience harassment within their jobs, the majority being women. As a starting point, this thesis aims to ignite future research about staff’s role in representation in Canada, our understanding of how gender influences this process, and staff’s overall involvement in Canadian politics.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.034
GPT teacher head0.304
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 teacher head, 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

Citations2
Published2019
Admission routes1
Has abstractyes

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