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Record W3181925075 · doi:10.55016/ojs/sppp.v12i1.56937

What’s New about Canada’s Feminist International Assistance Policy and Why ‘More of the Same’ Matters

2019· article· en· W3181925075 on OpenAlexafffundabout
Rebecca Tiessen

Bibliographic record

VenueThe School of Public Policy Publications · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Ottawa
FundersGovernment of Canada
KeywordsPolitical scienceSociology

Abstract

fetched live from OpenAlex

Assessments of Canada’s 2017 Feminist International Assistance Policy (FIAP) are mixed. There is widespread enthusiasm for the boldness of the policy, the innovation in represents (especially in its use of the language of feminism) and its commitment to increased funding and programmatic commitments to gender equality and women’s empowerment. Critical reviews of the policy also highlight some of its limitations in terms of missed opportunities to consider broader, intersectional feminist realities, lack of clarity in approach or definitions, and insufficient translation into practice and resource allocations. The innovations and limitations of the FIAP are examined here with an additional reflection on the implications of the focus on the novelty of this policy. An empirical study of the references to the policy’s innovation highlight several elements of ‘more of the same’ in terms of renewed gender mainstreaming commitments as well as missed opportunities. The argument advanced in this paper is that ‘more of the same’ is a double-barrelled assessment reflecting the possibilities it presents for broadening and deepening long-standing Canadian commitments to gender equality as well as challenges related to ongoing limitations and weaknesses. In the longer-term trajectory of Canadian commitments to gender equality, the FIAP offers an important next step – a step that reflects, overall, ‘more of the same’ rather than the innovative feminist vision it purports to be.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.302
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations8
Published2019
Admission routes3
Has abstractyes

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