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Record W3087541159 · doi:10.11575/prism/38201

Teachers’ Perceptions of Student Vulnerability and Risk: Considerations for School Social Work Practice

2020· dissertation· en· W3087541159 on OpenAlexaboutno aff
Stacey Lynn Marianchuk

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)Work (physics)PerceptionSocial workPedagogyPsychologyMathematics educationSociologyPolitical scienceEngineeringComputer scienceComputer security

Abstract

fetched live from OpenAlex

Risk and vulnerability are common terms used in education, yet there is limited research on teachers’ perceptions of student vulnerability and risk. This study uses the epistemological framework of social constructionism and qualitative research methodology of interpretative phenomenological analysis (IPA), to capture the essence of teachers’ perspectives that shape understandings of student vulnerability and risk as a way to inform school social work practice. Seven teachers from a large school district in Alberta participated in semi-structured interviews, garnering insights into their identities as teachers, navigating the complex lives of students, making sense of student risk and vulnerability, and ways to strengthen supports for students in schools. Considerations for school social workers as collaborative partners in schools are illuminated, with the hope that this research will inspire further research into school social work practice and training.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.015
Scholarly communication0.0110.005
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.147
GPT teacher head0.507
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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