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Record W3198121980 · doi:10.1177/14782103211041484

Investigating the challenges and opportunities of a bilingual equity knowledge brokering network: A critical and reflective perspective from university partners

2021· article· en· W3198121980 on OpenAlexaffabout
Nathalie Bélanger, Éliane Dulude

Bibliographic record

VenuePolicy Futures in Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEquity (law)Public relationsBridging (networking)SociologyPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Public education systems are often large, diverse, fragmented, and historically very hard to change. While previous reforms targeted primarily school staff, large-scale policies now include a broader audience including non-system organizations (e.g., knowledge brokering organizations) that may influence directly or indirectly policy implementation. Arguing that knowledge brokering organizations can contribute to policy implementation by bridging equity policy and research and practice at the local level, we put forward that their networks and relationships with districts, schools, and community organizations can bring about substantial changes to the organization and practices of schools on equity issues, even though they may face obstacles in implementing change due to particular contexts. We aim to better understand the role of knowledge brokering networks and of the university partners who act as knowledge brokers to bridge Ontario Ministry of Education policy goals with equity research and practice. As knowledge brokers working in a bilingual province-wide equity knowledge brokering network, we use our experiences as a particular case of a non-system role in system-wide reforms. We build on these experiences to question and self-reflect on our role as knowledge brokers who accompanied practitioners and community coalition leaders towards equity and inclusion over a 2-year period. By analyzing knowledge brokering functions, we show the challenges and opportunities we faced as knowledge brokers in guiding local equity and inclusion initiatives: 1) the roles we carried out during interactions and practices that could take on different meanings as knowledge producers and mobilizers; 2) we point out how and why these knowledge brokering functions and our roles within a bilingual province-wide network needed to adapt to local realities by providing for a more flexible planning process that allowed for sufficient time to identify local needs and to produce, if necessary, the knowledge that incorporated cultural context considerations or particularities.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.193
GPT teacher head0.484
Teacher spread0.291 · 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 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
Published2021
Admission routes2
Has abstractyes

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