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Record W4242270672 · doi:10.24124/2013/bpgub934

Building alliances to understanding and working with students affected by fetal alcohol spectrum disorder.

2013· dissertation· en· W4242270672 on OpenAlexaff
Sarah Deagle

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsCanadian HeritageUniversity of British ColumbiaLibrary and Archives Canada
Fundersnot available
KeywordsFetal Alcohol Spectrum DisorderPsychologyFetal alcoholDevelopmental psychologySocial psychologyAlcohol

Abstract

fetched live from OpenAlex

The purpose of this thesis was to examine the question, What are the differences in the attitudes and beliefs between Aboriginal and non-Aboriginal teachers with regards to students with fetal alcohol spectrum disorder (FASD)? I randomly selected Aboriginal (n=5) and non-Aboriginal teachers (n=5) to participate in a semi-structured interview and to complete a series of 13 vignettes with the researcher. The teachers represented five schools in northwestern BC three at the local high school and seven from four elementary schools. I assessed the interview data qualitatively and the vignettes quantitatively. The data revealed that there were many shared beliefs between the two groups of teachers. The differences were apparent in their variant orientations, or the nuances in behaviour. Many of the dominant orientations were similar between the two groups. I conclude the thesis with recommendations for further research and present my conclusions for the study. --Leaf ii.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.007
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.296
Teacher spread0.274 · 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
Published2013
Admission routes1
Has abstractyes

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