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Record W2973125501 · doi:10.11575/prism/36975

Advancing Healthy and Socially Just Schools and Communities: An Interdisciplinary Graduate Program

2019· article· en· W2973125501 on OpenAlexaboutno aff
Lynn Corcoran, Deinera Exner‐Cortens, Lana Wells

Bibliographic record

VenueOpen MIND · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPedagogyGraduate studentsPsychologySociologyHigher educationMathematics educationMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Advancing Healthy and Socially Just Schools and Communities is a four-course graduate certificate program collaboratively developed by an interdisciplinary team comprised of faculty from the fields of Social Work and Education at a Canadian university. The aim of this program is to facilitate systems-level change through enhancing the knowledge and skills of graduate students from disciplines such as social work, education, and nursing who work with youth in schools and communities. The ultimate goal of this systems-level change is promotion of healthy youth relationships and prevention of violence. The topics for the four courses in the program include the following: promoting healthy relationships and preventing interpersonal violence, recognizing and counteracting oppression and structural violence, addressing trauma and building resilience, and fostering advocacy and community in the context of social justice. The development and pedagogy of the certificate program are described, along with findings from a pilot study designed to examine the utility and feasibility of the initial certificate offering. Experiences with the program to date highlight the potential for improvements in graduate students’ attitudes, beliefs, and confidence regarding what constitutes violence and their role in responding to it.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.856

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.087
GPT teacher head0.448
Teacher spread0.361 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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