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Record W1848474196 · doi:10.20355/c5wc7t

Gender bias in education in Burkina Faso: Who pays the piper? Who calls the tune?

2006· article· en· W1848474196 on OpenAlexvenueno aff
Some Touorouzou

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policies and Family
Canadian institutionsnot available
Fundersnot available
KeywordsRedressEconomic growthAppropriationEmpowermentPolitical scienceGovernment (linguistics)Public relationsSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

In this chapter, I analyze policies developed by the government of Burkina Faso in order to redress an imbalance in gender education. Girls, in effect, are not getting their fair share of education, whether in quantity or quality. I critique existing policies concerning gender issues in education by first taking stock of different policies launched in favor of the education of girls, the context of their formation, and identify shortcoming therein. It has been found that international organizations, beyond their commitment to reverse the lag in the education of girls, bring with them an agenda that is at times contradictory with the aim of education for all. At the same time that governments are prodded to school all girls, Structural Adjustments Programs that generally bring more poverty and less public spending, are at loggerheads with increased access. Moreover, the policy choices of international organizations seem to be ill-equipped to subvert existing ideological and patriarchal structures. These structures do not allow for the empowerment of women. The government itself is found to have very little leverage on current policies, raising the nagging question of their appropriation. The paper ends with some policy recommendations that go beyond the construction of facilities and resources to address issues of the school experiences of girls, the curriculum-in-use, and overall problem of teacher training and compensation.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.048
GPT teacher head0.365
Teacher spread0.317 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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