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Accessing Services: Experiences of Families Living in Preston

2024· article· en· W4405014482 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Journal of Technology and Inclusive Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySociologyGerontologyMedicine

Abstract

fetched live from OpenAlex

Until the 1950s Canadian schools were segregated and a series of racist legislation was passed to keep Black Nova Scotians on the margins of society.Given the history of racist legislation and continued miseducation it is no surprise many students in the Preston Township experience academic disparities.There are services aimed to help families, however, these services have been described as complicated and difficult to navigate.The objective of this study was to explore how parents/guardians with students most affected by the opportunity gap make decisions about accessing services and their experience following through with their decision.A qualitative approach using Interpretive Phenomenological Analysis (IPA), combined with research reflexivity through a critical race lens was used.Seven semi-structured interviews were conducted with parents/guardians of students who access services.Transcripts were uploaded to NVivo for qualitative analysis.Parents/guardians represented students from pre-primary to grade 12. Three overarching themes were interpreted from the data.These included: The Struggle for Accountability, Cultural Inclusivity and Safety, and Barriers to accessing services.These findings provide insight into the reasons people choose to access services and their experience following through with their decisions.Our project contributes to strengthening the Preston Township voice around the need, provision, and experience of using services.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.344
Teacher spread0.338 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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